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In the area of task scheduling, in which the main focus was on the jobs' completion time, we propose in this work a scheduling optimization mechanism that aims to reduce the cost of task scheduling.
We argue not that our work contradicts these findings but rather we have identified an area of task performance that remains unaffected by colour cues.
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Therefore, much research has been done in the area of task-related management support in the past few years.
The IPC has been recognized as a prominent area of task-related deactivations [29], [32], [35].
Validity was assumed by structuring questions based on previous research in the areas of task motivation (Dörnyei, 2001, 2005; Dörnyei and Ushioda, 2011), student autonomy (Benson, 2007, 2013), and student feedback on language learning (Blackstone and Jaidev, 2015; Hafner and Miller, 2011; Takeda, 2015) The questions in each of these three areas are shown in Tables 3, 4 and 5 below.
To further investigate the mechanism of spontaneous control we undertook an exploratory analysis of changes in perfusion in the spontaneous group, contrasting exposure to emotional with neutral material, and using the areas of task activation and deactivation detected by perfusion imaging to aid interpretation (Figure 2B and Supporting Table S3 online).
To test for the dissociation predicted by the affect heuristic hypothesis, we looked for an interaction between sentence condition (emotional vs. neutral) and instruction (explicit instruction vs. spontaneous avoidance of negative sentences), using the areas of task activation and deactivation as an interpretive guide (Figure 2A and Supporting Table S2 in the online information).
We are currently engaged in studies to better understand the impact of mobile technology, and specifically eIMCI, on these areas of task-shifting, contributing to a functional electronic medical record, and improving the ability to update protocols in a timely manner.
Compared with previous studies in the area of physical task stress [19, 20, 22] and voice quality [28, 36, 39], this study uses a larger corpus of speech, with speakers of varying fitness levels, with significantly more inter-subject variability, while retaining low phonetic variability.
Right click an empty area of the task bar.
An example of an item measuring professional knowledge is: "I can provide an overview on the topics in the subject area of laboratory tasks (vs. English)".
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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