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On the light entertainment side of things, for example, we turn our noses up at Kim Kardashian: Hollywood and Candy Crush Saga; we're largely blind to the rise of Dubsmash; and we don't understand why Talking Tom Cat and his Talking Friends have become one of the biggest breakout brands in the apps world.
For this example, we turn to Barbie.
As an example, we turn to an image sequence of reflection high-energy electron diffraction (RHEED) data acquired during deposition of SrRuO3 on (001) SrTiO3.
As a second example, we turn now to an observational context using ILSA data where, importantly, random assignment to treatment conditions is not possible.
In the second numerical example, we turn to the consideration of the class of annuli having one or both boundaries featuring a polygonal contour.
For example, we turn getting ready to go to the park into a scavenger hunt.
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It used to be that most of the value we derived from our devices was the result of direct physical interaction: For example, we turned a key in a door look, flipped a light switch, or twisted the dial on a thermostat.
In the Borneo Eco-Challenge, for example, we turned a proposed hiking leg of the race into a swimming leg by jumping into the whitewater rapids and swimming for several hours downriver (just yards from the hiking trail) mostly in the dark.
We start with some basic definitions and key observations that are illustrated on a small example model, before we turn to the details of the method.
Once we have located the mechanism of action that is at work in the actual causal sequence of a Frankfurt example, we can turn our attention to understanding the dispositional features of it as a casual mechanism.
For example, we can turn a moderate Jew into an Episcopalian.
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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