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The method requires far less samples than both crude Monte Carlo simulation and the cross-entropy-based adaptive importance sampling method employing a unimodal density function; thus achieving relatively small values of the coefficient of variation efficiently.
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The problem is that current methods require far more energy than can be produced, at an eye-watering cost.
Ligand-based methods require far less protein than protein-based methods and are not limited by the size of a protein.
We, therefore, have to resort to a sophisticated optimization method, which requires far less computation.
Though the KOH method uses cheap reagents, it requires far more manipulation and time.
Documenting short-term fluctuations and linking them to changing socio-economic contexts requires far greater detail than past methods have provided [ 47].
We also introduce an alternative spatial discretization scheme for the self-adjoint angular intensity equation that requires far fewer unknowns than the standard method, but appears to give comparable accuracy.
This allows the snow to be used through the summer for refrigeration and air conditioning, which requires far less electricity than traditional cooling methods.
In contrast, the method proposed here does not rely on a dictionary and requires far fewer computational steps.
Most notably is that synchronous encryption requires far shorter encryption keys for equal security than asynchronous methods.
Statistical equating provides a robust standard-setting method, with a better theoretical foundation than judgemental techniques such as Angoff, and is more straightforward and requires far less examiner time to provide a more valid result.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com