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https://www.selleckchem.com/products/alkbh5-inhibitor-2.html The surface morphology of electrospun fibers largely determines their application scenarios. Conventional scanning electron microscopy is usually used to observe the microstructure of polymer electrospun fibers, which is time consuming and will cause damage to the samples. In this paper, we use backscattering Mueller polarimetry to classify the microstructural features of materials by statistical learning methods. Before feeding the Mueller matrix (MM) data into the classifier, we use a two-stage feature extraction method to find out representative polarization parameters. First, we filter out the irrelevant MM elements according to their characteristic powers measured by mutual information. Then we use Correlation Explanation (CorEx) method to group interdependent elements and extract parameters that represent their relationships in each group. The extracted parameters are evaluated by the random forest classifier in a wrapper forward feature selection way and the results show the effectiveness in classification performance, which also shows the possibility to detect nonporous electrospun fibers automatically in real time.Calibrating ring-based optical switches automatically is strongly demanded in large-scale ring-based optical switch fabrics. Supported by a machine-learning algorithm, we build an artificial neural network (ANN) model to retrieve the parameters of a 2×2 dual-ring assisted Mach-Zehnder interferometer (DR-MZI) switch from the measured spectra for the first time. The calibration algorithm is verified on several devices. The operating wavelength of the optical switch can be tuned to any wavelength in a free spectral range with an accuracy better than 90 pm. The extinction ratio exceeds 20 dB at the cross- and bar-states with no more than 7 calibration cycles. The voltage difference between the automatic calibration and manual tuning is less than 30 mV, showing the high accuracy of the calibr

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