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Here we will compare the point sets generated by our algorithms and those generated by the three samplers provided by lrt.
Besides, difference of the three samplers on the scope of the target analytes and exposure time, as well as the effects of environmental factors, e.g. hydrodynamic conditions, temperature, pH, ionic strength, DOM, on sampling performance were also introduced.
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A total of 2308 samples were collected from the two samplers.
We demonstrate however that a composite algorithm that strategically alternates between the two samplers' updates can be substantially faster than either individually.
In addition, there were no significant differences (p ≤ 0.05) in the concentrations of total nitrogen and total phosphorus measured from the pore water samples collected by the two samplers in situ from the seagrass bed.
The two samplers were compared for the volume of pore water collected in sandy and muddy substrates in the laboratory, and the concentrations of total nitrogen and total phosphorus in samples collected from a sandy substrate in Amphibolis and Posidonia seagrass meadows off the Adelaide coast.
For coprime sampling, the two samplers totally collect P+Q samples in PQT seconds, so the average sampling rate is f s, coprime = P + Q PQT = 1 PT + 1 QT < 1 T. (10).
For coprime sampling, the two samplers collect P + Q samples in PQT seconds, the average sampling rate is f s, coprime = P + Q PQT = 1 PT + 1 QT < 1 T (12).
The uptake rate (Ur), which is defined as: Ur = frac{D cdot A}{L} (2 where D is the diffusion coefficient (cm2/s) of the gaseous species, A is the cross-sectional area (cm2) and L the diffusive path (cm), is quite different for the two samplers.
The two samplers were 600 and 2800 m away from the industrial core and 8200 m apart.
Among the Gibbs samplers that were considered, the most efficient sampler is about 2.1 times as efficient as the MH algorithm proposed by Meuwissen et al. and 1.7 times as efficient as that proposed by Habier et al. The three Gibbs samplers presented here were twice as efficient as Metropolis–Hastings samplers and gave virtually the same results.
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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