This expression has been used in previous studies on numerical optimization of spatial sampling, the objective of this study was to use it to discover simple rules of thumb for practical geostatistical sampling.
Furthermore by reweighting an initial set of samples, the objective and constraint functions become smooth functions of changes of the parameters of probability distribution, rather than the stochastic functions obtained using MCS.
After obtaining a certain number of initial samples, we select the weight of the sample as the objective function of the optimization and present the differential evolution algorithm to obtain valid samples rather than perform initial sample filtering and resampling.
Because of the strong field between the sample and the objective lens, even a small tilt of the sample generates a parasitic dipole field, which decreases resolution and causes image deformations.
However, in some cases, like in the cathode lens between the sample and the objective lens in the photoemission electron microscope, even a small imperfection can impair the resolution significantly.
To induce photobleaching and/or photo-unbinding the laser (488, 489, 543, 561 or 633 nm for 1PE or a modelocked Titan-sapphire laser line at 800 nm, 200 fs pulses, 80 MHz for 2PE) was focused onto the CaM/CKII peptide coated glass surface or the cell sample through the objective lens (Zeiss, Plan-Apochromat 63×/1.40 Oil DIC M27).
TLM is based on the set-up where the light is transmitted from a source on the opposite side of the sample from the objective.
We have introduced the NL-GS concept to correct the measured aberrations induced by the sample and the objective in a single-step.
Typically, this problem is resolved by alternating between scanning and moving the sample or the objective in order to acquire an image from another plane.
Both of the two beams from the two devices were focused on the sample by the objective lenses (f = 100 mm), which gave the lateral resolution of ~20 μm [ 10].
