https://www.selleckchem.com/products/cpi-613.html In most of the final results, the average total errors between the reconstructed 3D surface and the CAD geometry are less than 0.1 mm.A new method for profile measurements of small transverse size beams by means of a vibrating wire is presented. A vibrating wire resonator with a new magnetic system was developed and manufactured to ensure that the wire oscillated in a single plane. Presented evidence gives us confidence that the autogenerator creates vibrations at the natural frequency of the wire in a plane of the magnetic system, and these vibrations are sinusoidal. The system for measuring the laser beam reflected from the vibrating wire by means of a fast photodiode was upgraded. The experiments allowed the reconstruction of a fine structure of the focused beam of the semiconductor laser using only a few vibrating wire oscillations. The system presented here would eventually enable the implementation of tomographic measurements of the thin beam profile.We present a framework for training artificial neural networks (ANNs) as surrogate Bayesian models for the inference of plasma parameters from diagnostic data collected at nuclear fusion experiments, with the purpose of providing a fast approximation of conventional Bayesian inference. Because of the complexity of the models involved, conventional Bayesian inference can require tens of minutes for analyzing one single measurement, while hundreds of thousands can be collected during a single plasma discharge. The ANN surrogates can reduce the analysis time down to tens/hundreds of microseconds per single measurement. The core idea is to generate the training data by sampling them from the joint probability distribution of the parameters and observations of the original Bayesian model. The network can be trained to learn the reconstruction of plasma parameters from observations and the model joint probability distribution from plasma parameters and observations. Prev