Search

The pre-treatment stage of wastewater treatment plants (WWTP), where most of the larger waste, including sand and fat, is removed, is of great importance for the performance and durability of these plants. This work develops a model that predicts the sand content that reaches the plant. For this purpose, data were collected from one operation year of the Villap&eacute, rez Wastewater Treatment Plant located in the northeast of the city of Oviedo (Asturias, Spain) and the MARS (Multivariate Adaptive Regression Splines) method was used for modelling. The accuracy of the MARS model developed using the determination coefficient is R2 = 0.74 for training data and R2 = 0.70 in validation data. These results indicate that it is possible to predict trend changes in sand production as a function of input variables changes such as flow rate, pH, ammonia, etc. This will prevent the plant from possible operational problems, as actions could be taken, such as starting up more pre-treatment lines or emptying the containers, so that the arrival of the sand can be assumed without any problem. In this way, the possibility of letting sand contents over the established limits pass that could affect the following processes of the treatment plant is avoided.

WOS: 000425082600130 International migration significantly affects economic, social, cultural, and political factors of the country. Owing to this situation, it can be said that the reasons of international migration should be analyzed in order to control this problem. The purpose of this study is to determine the influencing factors of international migration in Azerbaijan. In this scope, annual data of 11 explanatory variables for the period of 1995-2015 was analyzed via Multivariate Adaptive Regression Splines (MARS) method. According to the results of this analysis, it was identified that people prefer to move other countries in case of high unemployment rates. In addition, the results of the study show that population growth and high mortality rate increases the migration level. While considering these results, it was recommended that Azerbaijan should focus on these aspects to control international migration problem.

Higher unemployment rate is the problem of most of the countries in the world. Because of this situation, these countries try to make many actions in order to decrease unemployment rate. However, to make such a recommendation, first of all, the reasons of the unemployment should be analyzed. Within this framework, the aim of this study is to identify the factors which influence unemployment in Turkey. For this purpose, quarterly data for the periods between 2003 and 2016 is evaluated with MARS method. It is concluded that economic growth negatively affects unemployment in Turkey. Another result of this study is that higher inflation rates negatively affect unemployment rate. The last conclusion of this analysis is that interest rate has a positive influence on the unemployment rate. While considering these results, it is recommended that economic performance of the country should be improved and interest rates should be declined to decrease unemployment rate in Turkey. Another recommendation is that implementations, which are aimed to decrease inflation rate, should be controlled carefully because any implementation which aims to decrease inflation rate causes unemployment rate to increase.

The aim of this study is to identify the determinants of US Dollar/Turkish Lira currency exchange rate for strategic decision making in the global economy. Within this scope, quarterly data for the period between 1988:1 and 2016:2 was used in this study. In addition to this aspect, 10 explanatory variables were considered in order to determine the leading indicators of US Dollar/Turkish Lira currency exchange rate. Moreover, Multivariate Adaptive Regression Splines (MARS) method was used so as to achieve this objective. According to the results of this analysis, it was defined that two different variables affect this exchange rate in Turkey. First of all, it was identified that there is a negative relationship between current account balance and the value of US Dollar/Turkish Lira currency exchange rate. This result shows that in case of current account deficit problem, Turkish Lira experiences depreciation. Furthermore, it was also concluded that when there is an economic growth in Turkey, Turkish Lira increases in comparison with US Dollar. While taking into the consideration of these results, it could be generalized that emerging economies such as Turkey have to decrease current account deficit and investors should focus on higher economic growth in order to prevent the depreciation of the money in the strategic investment decision.

At the initial stage of liquid injection into a gas, the injection liquid has not yet been broken up, and at a low surrounding pressure of less than about 0.1 MPa, the tip of the injection liquid forms the shape of a thin string. This phenomenon and the reason for the shape have not been well studied. At a higher pressure of about 3.0 MPa, the tip of the flow forms a mushroom shape. In this paper, the MARS method for simulating free surfaces is applied to analyze the initial shape of the injection liquid. The above phenomena are reproduced and the reason for them is clarified. Another focus in this paper is the throttling effect due to the nozzle, which causes the formation of small air bubbles near the nozzle wall. These bubbles induce large eddies at the surface of the liquid column and promote the disintegration of the liquid film.

International audience; In this paper, an on-line energy estimation model has been implemented on an RVC-CAL HEVC decoder. The model is driven by Performance Monitoring Counters and fitted by the MARS method. The estimation results achieve average relative errors less than 10%. In addition, the model computation overhead is less than 0.5%. The model might be employed within the RVC framework to provide an energy-aware decoder reconfiguration.

In this thesis we propose probabilistic and statistic models based on multidimensional data for forecasting uncertainty on aircraft trajectories. Assuming that during the flight, aircraft follows his 3D trajectory contained into his initial flight plan, we used all characteristics of flight environment as predictors to explain the crossing time of aircraft at given points on their planned trajectory. These characteristics are: weather and atmospheric conditions, flight current parameters, information contained into the flight plans and the air traffic complexity. Typically, in this study, the dependent variable is difference between actual time observed during flight and planned time to cross trajectory planned points: this variable is called temporal difference. We built four models using method based on partitioning recursive of the sample. The first called classical CART is based on Breiman CART method. Here, we use regression trees to build points typology of aircraft trajectories based on previous characteristics and to forecast crossing time of aircrafts on these points. The second model called amended CART is the previous model improved. This latter is built by replacing forecasting estimated by the mean of dependent variable inside the terminal nodes of classical CART by new forecasting given by multiple regression inside these nodes. This new model developed using Stepwise algorithm is parcimonious because for each terminal node it permits to explain the flight time by the most relevant predictors inside the node. The third model is built based on MARS (Multivariate adaptive regression splines) method. Besides continuity of the dependent variable estimator, this model allows to assess the direct and interaction effects of the explanatory variables on the crossing time on flight trajectory points. The fourth model uses boostrap sampling method. It’s random forests where for each bootstrap sample from the initial data, a tree regression model is built like in CART method. The general model forecasting is obtained by aggregating forecasting on the set of trees. Despite the overfitting observed on this model, it is robust and constitutes a solution against instability problem concerning regression trees obtained from CART method. The models we built have been assessed and validated using data test. Their using to compute the sector load forecasting in term to aircraft count entering the sector shown that, the forecast time horizon about 20 minutes with the interval time larger than 20 minutes, allowed to obtain forecasting with relative errors less than 10%. Among all these models, classical CART and random forests are more powerful. Hence, for regulator authority these models can be a very good help for managing the sector load of the airspace controlled.; Dans cette thèse, nous proposons des modèles probabilistes et statistiques d’analyse de données multidimensionnelles pour la prévision de l’incertitude sur les trajectoires d’aéronefs. En supposant que pendant le vol, chaque aéronef suit sa trajectoire 3D contenue dans son plan de vol déposé, nous avons utilisé l’ensemble des caractéristiques de l’environnement des vols comme variables indépendantes pour expliquer l’heure de passage des aéronefs sur les points de leur trajectoire de vol prévue. Ces caractéristiques sont : les conditions météorologiques et atmosphériques, les paramètres courants des vols, les informations contenues dans les plans de vol déposés et la complexité de trafic. Typiquement, la variable dépendante dans cette étude est la différence entre les instants observés pendant le vol et les instants prévus dans les plans de vol pour le passage des aéronefs sur les points de leur trajectoire prévue : c’est la variable écart temporel. En utilisant une technique basée sur le partitionnement récursif d’un échantillon des données, nous avons construit quatre modèles. Le premier modèle que nous avons appelé CART classique est basé sur le principe de la méthode CART de Breiman. Ici, nous utilisons un arbre de régression pour construire une typologie des points des trajectoires des vols en fonction des caractéristiques précédentes et de prévoir les instants de passage des aéronefs sur ces points. Le second modèle appelé CART modifié est une version améliorée du modèle précédent. Ce dernier est construit en remplaçant les prévisions calculées par l’estimation de la moyenne de la variable dépendante dans les nœuds terminaux du modèle CART classique par des nouvelles prévisions données par des régressions multiples à l’intérieur de ces nœuds. Ce nouveau modèle développé en utilisant l’algorithme de sélection et d’élimination des variables explicatives (Stepwise) est parcimonieux. En effet, pour chaque nœud terminal, il permet d’expliquer le temps de vol par des variables indépendantes les plus pertinentes pour ce nœud. Le troisième modèle est fondé sur la méthode MARS, modèle de régression multiple par les splines adaptatives. Outre la continuité de l’estimateur de la variable dépendante, ce modèle permet d’évaluer les effets directs des prédicteurs et de ceux de leurs interactions sur le temps de passage des aéronefs sur les points de leur trajectoire de vol prévue. Le quatrième modèle utilise la méthode d’échantillonnage bootstrap. Il s’agit notamment des forêts aléatoires où pour chaque échantillon bootstrap de l’échantillon de données initial, un modèle d’arbre de régression est construit, et la prévision du modèle général est obtenue par une agrégation des prévisions sur l’ensemble de ces arbres. Malgré le surapprentissage observé sur ce modèle, il est robuste et constitue une solution au problème d’instabilité des arbres de régression propre à la méthode CART. Les modèles ainsi construits ont été évalués et validés en utilisant les données test. Leur application au calcul des prévisions de la charge secteur en nombre d’avions entrants a montré qu’un horizon de prévision d’environ 20 minutes pour une fenêtre de temps supérieure à 20 minutes permettait d’obtenir les prévisions avec des erreurs relatives inférieures à 10%. Parmi ces modèles, CART classique et les forêts aléatoires présentaient de meilleures performances. Ainsi, pour l’autorité régulatrice des courants de trafic aérien, ces modèles constituent un outil d’aide pour la régulation et la planification de la charge des secteurs de l’espace aérien contrôlé.