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One of the major tasks in this study was to collect data used to develop a land use/transport model.
The tasks in this study, as well as the scoring system, were based on the studies of Derossis et al. [17] and are shown in Fig. 1.
The GI tasks in this study conform to these criteria since learners attend to meaning to notice the function of the grammatical structures in use.
One of the most important tasks in this study is to analyze and predict their properties such as solubility, optical and fluorescence emission properties and thermal stability with respect to their chemical structure.
The important tasks in this study are to analyze and predict the properties such as solubility, optical and fluorescence emission properties, biological activities and thermal stability with respect to their chemical structures.
The study is innovative in that the tasks in this study involve speaking in a high-stakes language assessment context but they also build on a regular reading and viewing programme integrated into the school curriculum.
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As shown in Fig. 1, the pivoting task in this study was performed on an almost extended leg.
Three approaches are used to perform this task in this study: hard classification (HC); fuzzy c-means (FCM); and genetic algorithms (GA).
This finding is the more robust because the task in this study was more demanding than in the study of Newell et al. [2] and yet the additional difficulty of object rotation had little effect on cross-modal recognition.
We adopted Paulus et al.'s (2003) Risky-Gains task in this study.
Therefore, we applied a motor task in this study to facilitate motor cognitive demand.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com