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Other than using infinite basis sets, two methods exist to eliminate the BSSE. In the chemical Hamiltonian approach (CHA), [4] [5] basis set mixing is prevented a priori, by replacing the conventional Hamiltonian with one in which all the projector-containing terms that would allow mixing have
The main electrolytes are confined to water phase. So for example in 10 μL plasma sample, only 9.3 μL is water that contains the electrolyte. Thus if the concentration of an electrolyte, say Na + is determined to be 140 mmol/L, it is the concentration in total plasma volume, not in plasma water volume. [1]
Endomicroscopy is a technique for obtaining histology-like images from inside the human body in real-time, [1] [2] [3] a process known as ‘optical biopsy’. [ 4 ] [ 5 ] It generally refers to fluorescence confocal microscopy , although multi-photon microscopy and optical coherence tomography have also been adapted for endoscopic use.
The handbook was originally published in 1928 by the Chemical Rubber Company (now CRC Press) as a supplement (Mathematical Tables) to the CRC Handbook of Chemistry and Physics. Beginning with the 10th edition (1956), it was published as CRC Standard Mathematical Tables and kept this title up to the 29th edition (1991).
Any non-linear differentiable function, (,), of two variables, and , can be expanded as + +. If we take the variance on both sides and use the formula [11] for the variance of a linear combination of variables (+) = + + (,), then we obtain | | + | | +, where is the standard deviation of the function , is the standard deviation of , is the standard deviation of and = is the ...
Confocal endoscopy, or confocal laser endomicroscopy (CLE), is a modern imaging technique that allows the examination of real-time microscopic and histological features inside the body. In the word "endomicroscopy", endo- means "within" and -skopein means "to view or observe".
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If the two statistics differ significantly then that indicates the model has been over-parameterized, so that to some extent it predicts not the ideal error-free data for the correct model, but rather the error-afflicted data actually observed.