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La thèse vise à identifier, dans un premier temps, l’influence du timing de versement des dividendes des sociétés françaises cotées sur les cours boursiers. Elle cherche à identifier, dans un deuxième temps, les déterminants du timing de versement des dividendes. La démarche retenue pour argumenter ces propos est la suivante : dans une première partie, nous avons posé notre cadre théorique. Le positionnement de la thèse dans l’ancrage de la théorie politico-contracteulle et la théorie des signaux nous a orienté vers l’étude du contenu informationnel du timing de versement des dividendes dans un premier temps et à identifier ses déterminants dans un deuxième temps. La seconde partie est consacrée à l’étude empirique réalisée auprès de 69 entreprises initiatrices de dividendes cotées à l’indice SBF 120 durant l’année 2007 afin de répondre à notre premier objectif, et portée sur un échantillon de 57 sociétés françaises distributrices d’un dividende annuel durant la période 2003-2009 afin de répondre à notre second objectif. S’agissant de notre premier objectif, le recours à la méthodologie des études d’évènement a révélé que les cours réagissent à la date de versement des dividendes ce qui confirme que le timing de versement des dividendes possède un contenu informationnel. Quant à notre second objectif, les dispositions réglementaires souples sur la fixation de la date de versement du dividende et son emploi en tant que signal émis de la firme vers le marché posent la question du choix de cette date dans le contexte français à système juridique civil connu pour la protection des intérêts des actionnaires minoritaires. Les résultats de la littérature antérieure restent timides en raison de leur focalisation sur la date de versement du premier dividende – et notamment sur la probabilité d’initier un dividende suite à l’introduction en bourse. Les résultats de notre étude empirique confirment l’impact significatif de la présence d’un actionnaire majoritaire, de la profitabilité, de la liquidité et de la durée de versement du dividende précédent sur la fixation de la date de versement de cette année. Cet impact semble se manifester à travers une limitation de la durée entre la date de l’assemblée des actionnaires et la date de paiement effectif des dividendes et une reconnaissance de cette durée comme étant une bonne nouvelle par rapport aux autres signaux émis par l’entreprise au marché boursier. / The purpose of this study is to identify the informational content of the dividend pay date and its determinants. Namely, is there information in the timing of the dividend payments? The empirical evidence indicates that the market reacts at the dividend pay date. Mean excess returns of stock prices on the pay date are significantly positive and are insignificant and negative around the entire population of dividend pay dates. On the other side we are interested in the determinants of the dividend pay date. Our multivariate analysis shows that the ownership structure, the liquidity of the firm, the result, and the previous timing of dividend payment influence the fixing of the dividend pay date. This impact is shown as shorten as the delay between the date of the general meeting and the dividend pay date. This duration is considered as good news and can be a signal employed to attract new investors in the stock market.

The enterprise finance crisis forecast could provide alarm to managers and investors of the enterprise, many scholars advised different alarm models to explain and predict the enterprise is facing finance crisis or not. These models can be classified into three categories by analysis method, the first is single-variate model, it¡¦s easy to implement. The second is multi-variate model which need to fit some statistical assumption, and the third is Artificial Neural Network model which doesn¡¦t need to fit any statistical assumption. However, these models do not consider the industrial effect, different industry could have different finance crisis pattern. This study uses the advantage of Artificial Neural Network to build the process of the enterprise finance crisis forecast model, because it doesn¡¦t need to fit any statistical assumption. Finally, the study use reality finance data to prove the process, and compare with the other models. The result shows the model issued by this study is suitable in Taiwan Electronic Industry, but the performance in Taiwan architecture industry is not better than other models.

Advanced Traveler Information System (ATIS) is one component of Intelligent Transportation Systems (ITS), and a major component of ATIS is travel time information. The provision of timely and accurate transit travel time information is important because it attracts additional ridership and increases the satisfaction of transit users. The cost of electronics and components for ITS has been decreased, and ITS deployment is growing nationwide. Automatic Vehicle Location (AVL) Systems, which is a part of ITS, have been adopted by many transit agencies. These allow them to track their transit vehicles in real-time. The need for the model or technique to predict transit travel time using AVL data is increasing. While some research on this topic has been conducted, it has been shown that more research on this topic is required. The objectives of this research were 1) to develop and apply a model to predict bus arrival time using AVL data, 2) to identify the prediction interval of bus arrival time and the probabilty of a bus being on time. In this research, the travel time prediction model explicitly included dwell times, schedule adherence by time period, and traffic congestion which were critical to predict accurate bus arrival times. The test bed was a bus route running in the downtown of Houston, Texas. A historical based model, regression models, and artificial neural network (ANN) models were developed to predict bus arrival time. It was found that the artificial neural network models performed considerably better than either historical data based models or multi linear regression models. It was hypothesized that the ANN was able to identify the complex non-linear relationship between travel time and the independent variables and this led to superior results. Because variability in travel time (both waiting and on-board) is extremely important for transit choices, it would also be useful to extend the model to provide not only estimates of travel time but also prediction intervals. With the ANN models, the prediction intervals of bus arrival time were calculated. Because the ANN models are non parametric models, conventional techniques for prediction intervals can not be used. Consequently, a newly developed computer-intensive method, the bootstrap technique was used to obtain prediction intervals of bus arrival time. On-time performance of a bus is very important to transit operators to provide quality service to transit passengers. To measure the on-time performance, the probability of a bus being on time is required. In addition to the prediction interval of bus arrival time, the probability that a given bus is on time was calculated. The probability density function of schedule adherence seemed to be the gamma distribution or the normal distribution. To determine which distribution is the best fit for the schedule adherence, a chi-squared goodness-of-fit test was used. In brief, the normal distribution estimates well the schedule adherence. With the normal distribution, the probability of a bus being on time, being ahead schedule, and being behind schedule can be estimated.

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<div> <div> <div> <p>As the demand and complexity of air traffic increase, it becomes crucial to maintain the safety and efficiency of the operations in airspaces, which, however, could lead to an increased workload for Air Traffic Controllers (ATCs) and delays in their decision-making processes. Although terminal airspaces are highly structured with the flight procedures such as standard terminal arrival routes and standard instrument departures, the aircraft are frequently instructed to deviate from such procedures by ATCs to accommodate given traffic situations, e.g., maintaining the separation from neighboring aircraft or taking shortcuts to meet scheduling requirements. Such deviation, called vectoring, could even increase the delays and workload of ATCs. This thesis focuses on developing a framework for trajectory pattern identification and classification that can provide ATCs, in vectored airspace, with real-time information of which possible vectoring pattern a new incoming aircraft could take so that such delays and workload could be reduced. This thesis consists of two parts, trajectory pattern identification and trajectory pattern classification. </p> <p>In the first part, a framework for trajectory pattern identification is proposed based on agglomerative hierarchical clustering, with dynamic time warping and squared Euclidean distance as the dissimilarity measure between trajectories. Binary trees with fixes that are provided in the aeronautical information publication data are proposed in order to catego- rize the trajectory patterns. In the second part, multiple recurrent neural network based binary classification models are trained and utilized at the nodes of the binary trees to compute the possible fixes an incoming aircraft could take. The trajectory pattern identifi- cation framework and the classification models are illustrated with the automatic dependent surveillance-broadcast data that were recorded between January and December 2019 in In- cheon international airport, South Korea . </p> </div> </div> </div>

京都大学 / 0048 / 新制・課程博士 / 博士(人間・環境学) / 甲第19794号 / 人博第765号 / 新制||人||184(附属図書館) / 27||人博||765(吉田南総合図書館) / 32830 / 京都大学大学院人間・環境学研究科共生人間学専攻 / (主査)教授 神﨑 素樹, 教授 森谷 敏夫, 教授 石原 昭彦 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DGAM

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Around the world bridges are ageing. In Europe approximately two thirds of all railway bridges are over 50 years old. As these structures age, it becomes increasingly important that they are properly maintained. If damage remains undetected this can lead to premature replacement which can have major financial and environmental costs. It is also imperative that bridges are kept safe for the people using them. Thus, it is necessary for damage to be detected as early as possible. This research investigates an unsupervised, model-free damage detection method which could be implemented for continuous structural health monitoring. The method was based on past research by Gonzalez and Karoumi (2015), Neves et al. (2017) and Chalouhi et al. (2017). An artificial neural network (ANN) was trained on accelerations from the healthy structural state. Damage sensitive features were defined as the root mean squared errors between the measured data and the ANN predictions. A baseline healthy state could then be established by presenting the trained ANN with more healthy data. Thereafter, new data could be compared with this reference state. Outliers from the reference data were taken as an indication of damage. Two outlier detection methods were used: Mahalanobis distance and the Kolmogorov-Smirnov test. A model steel bridge with a span of 5 m, width of 1 m and height of approximately 1.7 m was used to study the damage detection method. The use of an experimental model allowed damaged to be freely introduced to the structure. The structure was excited with a 12.7 kg rolling mass at a speed of approximately 2.1 m/s (corresponding to a 20.4 ton axle load moving at 47.8 km/h in full scale). Seven accelerometers were placed on the structure and their locations were determined using an optimal sensor placement algorithm. The objectives of the research were to: identify a number of single damage cases, distinguish between gradual damage cases and identify the location of damage. The proposed method showed promising results and most damage cases were detected by the algorithm. Sensor density and the method of excitation were found to impact the detection of damage. By training the ANN to predict correlations between accelerometers the sensor closest to the damage could be detected, thus successfully localising the damage. Finally, a gradual damage case was investigated. There was a general increase in the damage index for greater damage however, this did not progress smoothly and one case of ‘greater’ damage showed a decrease in the damage index.

<p dir="ltr">The research focuses on improving natural language processing (NLP) models by integrating the hierarchical structure of language, which is essential for understanding and generating human language. The main contributions of the study are:</p><ol><li><b>Hierarchical RNN Model:</b> Development of a deep Recurrent Neural Network model that captures both explicit and implicit hierarchical structures in language.</li><li><b>Hierarchical Attention Mechanism:</b> Use of a multi-level attention mechanism to help the model prioritize relevant information at different levels of the hierarchy.</li><li><b>Latent Indicators and Efficient Training:</b> Integration of latent indicators using the Expectation-Maximization algorithm and reduction of computational complexity with Bootstrap sampling and layered training strategies.</li><li><b>Sequence-to-Sequence Model for Translation:</b> Extension of the model to translation tasks, including a novel pre-training technique and a hierarchical decoding strategy to stabilize latent indicators during generation.</li></ol><p dir="ltr">The study claims enhanced performance in various NLP tasks with results comparable to larger models, with the added benefit of increased interpretability.</p>