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Abstract

The author analyses neologisms of M. Sholokhov in relation to their translation into the German language. Then the quality of the translation is determined. In order to evaluate the translations three criteria are adopted: the degree of semantic closeness between the newly created units and their translation equivalents, their expressiveness and vividness. Based on these criteria the author evaluates the accuracy of the German translations.

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Authors and Affiliations

Marek Marszałek
ORCID: ORCID
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Abstract

The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and unevenness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V 4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.
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Authors and Affiliations

Elisabete Freitas
Joaquim Tinoco
Francisco Soares
Jocilene Costa
Paulo Cortez
Paulo Pereira

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