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Humanistic is an approach recommended by the ideas of intellectuals, and Scholars like Erickson, Roger, and Maslow began to infuse the field of second language teaching and learning towards the end of 1970. According to Lei (2007), the humanistic approach emphasizes the importance of the inner world of the learner and places the individual's thoughts, emotions, and feelings at the limelight of all human development. As a result of this new shift of focus, language education, and pedagogy changed away from the previous behavioristic and mentality approaches, and consequently, a new kind of education known as humanistic education emerged. Besides, significant changes occurred in all aspects of language education; that is, the traditional roles of teachers and learners redefined, and learner-centered classrooms replaced the previously authoritarian teaching practices. This paper is two-fold. Firstly, it is going to take a detailed look at the main principles and features of humanistic education, and secondly, it aims at discussing the implications and applications of humanistic education. Lastly, it tries to clarify the new roles and responsibilities considered for language teachers to be able to engage the students in the learning process fully.

Humanistic is an approach recommended by the ideas of intellectuals, and Scholars like Erickson, Roger, and Maslow began to infuse the field of second language teaching and learning towards the end of 1970. According to Lei (2007), the humanistic approach emphasizes the importance of the inner world of the learner and places the individual's thoughts, emotions, and feelings at the limelight of all human development. As a result of this new shift of focus, language education, and pedagogy changed away from the previous behavioristic and mentality approaches, and consequently, a new kind of education known as humanistic education emerged. Besides, significant changes occurred in all aspects of language education; that is, the traditional roles of teachers and learners redefined, and learner-centered classrooms replaced the previously authoritarian teaching practices. This paper is two-fold. Firstly, it is going to take a detailed look at the main principles and features of humanistic education, and secondly, it aims at discussing the implications and applications of humanistic education. Lastly, it tries to clarify the new roles and responsibilities considered for language teachers to be able to engage the students in the learning process fully.

Accidents on the runway triggered the development and implementation of mitigation strategies. Therefore, the airline industry is moving toward proactive risk management, which aims to identify and predict risk percursors and to mitigate risks before accidents occur. For certain predictions Machine Learning techniques can be used. Although many studies have explored and applied novel Machine Learning techniques on different aircraft Radar and operational Taxi data, the identification and prediction of abnormal Runway Occupancy Times and the observation of related percursors are not well developed. In our previous papers, three feasible methods were introduced: Lasso, Multi-Layer Perceptiona and Neural Networks to predict the Taxi-Out Time on the taxiway and the time to Fly and True Airspeed profile on final approach. This paper presents a new Machine Learing method, where we merge these feasible Machine Learning techniques for prediction the abnormal Runway Occupancy times of unique radar data patterns. Additionally we use in this study the Regression Tree method to observe key related precursors extracted from the top 10 features. Compared with existing methods, the new method no longer requires predefined criteria or domain knowledge. Tests were conductioned using runway and final approach aircraft radar data consisting of 78,321 Charles de Gaulle flights and were benchmarked against 500,000 Vienna flights., Aerospace Transport & Operations

Accidents on the runway triggered the development and implementation of mitigation strategies. Therefore, the airline industry is moving toward proactive risk management, which aims to identify and predict risk percursors and to mitigate risks before accidents occur. For certain predictions Machine Learning techniques can be used. Although many studies have explored and applied novel Machine Learning techniques on different aircraft Radar and operational Taxi data, the identification and prediction of abnormal Runway Occupancy Times and the observation of related percursors are not well developed. In our previous papers, three feasible methods were introduced: Lasso, Multi-Layer Perceptiona and Neural Networks to predict the Taxi-Out Time on the taxiway and the time to Fly and True Airspeed profile on final approach. This paper presents a new Machine Learing method, where we merge these feasible Machine Learning techniques for prediction the abnormal Runway Occupancy times of unique radar data patterns. Additionally we use in this study the Regression Tree method to observe key related precursors extracted from the top 10 features. Compared with existing methods, the new method no longer requires predefined criteria or domain knowledge. Tests were conductioned using runway and final approach aircraft radar data consisting of 78,321 Charles de Gaulle flights and were benchmarked against 500,000 Vienna flights., Air Transport & Operations

currently, at many airports, the runway throughput is the limiting factor for the overall capacity. Among the most important constraining parameters is the separation minima expressed in distance. On the top of these minima, the difference of the leader and follower aircraft speed profiles imposes to consider buffer to cope with compression effect. Currently, Air Traffic Control Officers (ATCO’s) take these buffers on the basis of their training and experience. However, this experience will not be sufficient to safety deploy advanced concepts, like pair-wise separations, that increase variability in the separations to be delivered and therefore in the compression buffer to be considered. Systematic analysis of years of radar tracks has allowed to better predict the buffers to apply by characterising the time to fly (T2F) given a separation distance and True Airspeed (TAS) profile as a function of meteorological parameters. This paper presents how Machine Learning (ML) techniques may be used for predicting the T2F and TAS profile on final approach. Different ML techniques will be assessed on their forecast performance, computational time and amount of data needed for delivering a reliable prediction. The techniques will be applied on 2 different major European airports traffic and will be benchmarked against Optimized Runway Delivery (ORD) study using a Model Based Approach (MBA) for deriving the T2F and TAS. As a result the most efficient ML techniques will be applied on two case studies for predicting the T2F and TAS., Air Transport & Operations

This paper presents a severity metric supporting the characterization of the effect of a wake vortex encounter on a follower aircraft. The proposed metric is the Rolling Moment Coefficient computed using various simplified assumptions that lead to a simple and usable metric. The metric can indeed be computed using available aircraft data. The paper also presents the assessment of the metric based on the results of a wake vortex encounter flight test campaign performed by Airbus. The use of the proposed metric yields good agreement with the experimental data. Because it is simple, yet realistic compared to experimental data, the metric can be used in a relative safety analysis permitting a reduction of the wake turbulence separations compared to today’s ICAO standard, as was done in the framework of the RECAT-EU project.

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Status of studies of numerical methods, analog and digital, in support of the OPTIR code are reported. The possibility of Treanor-type algorithms with expanded stability regions is established. Laumbach and Probstein treatment of strong shocks in the exponential atmosphere is extended to the more general case of an atmospheric density with an arbitrary vertical profile. (Author)

The report, Volume 3 of the OPTIR 2 computer code describes various models employed in the code.

A model is considered for the structure of a plane, steady shock wave propagating in an inviscid plasma across a magnetic field normal to the direction of propagation. The model does not possess a solution for relatively small magnetic fields. Assuming that a continuous solution exists in this case when the effects of viscosity are taken into account, it is shown how the occurrence of a sub-shock can be conjectured when these effects become vanishingly small. (Author)

The abscissae and weights of the Gauss-Laguerre quadrature formula are given to 18 places.

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Appartient à l’ensemble documentaire : FranceJp0, Appartient à l’ensemble documentaire : MAEDIGen0, Appartient à l’ensemble documentaire : MAEDI005