Approximately half of the IBD patients relapsed within 2 years after anti-TNF discontinuation. In CD patients, no difference between those who were or were not in deep remission was found. Colonic localization protected patients from relapse.
K-a emission is an intense short-pulse line source well suited for X-ray diagnostic techniques with subpicosecond and micrometer resolution. Numerical simulations are performed here in a search for laser-target interaction regimes where both high efficiency of laser energy transformation to X-ray emission and ultrashort X-ray pulses are achieved. We use the one-dimensional PIC code for the description of the laser interaction with the plasma layer at the target surface. Fast electron transport into the target is treated by our newly developed Monte Carlo code with temporal resolution that is described here in detail. Our simulations reveal extremely short ;200 fs FWHM bright K-a X-ray pulses emitted from targets heated by 120-fs pulses of a table-top laser. Laser energy conversion efficiency to K-a line emission as high as 6 ϫ10 Ϫ5 is noticed. Integration of the emitted energy over the focal spot is carried out to improve the simulation accord with published experimental data. Negligible impact of self-induced electric fields on K-a emission is found for conducting target materials at moderate laser intensities Շ10 17 W0cm 2 .
Product quality is a crucial issue for manufacturing companies, so it is essential to take note of any emerging product defects. In contrast to the use of traditional methods, the "modern" constantly evolving data mining methods are now being more frequently used. The main objective of this paper is to detect the potential cause or the area of the production process where the majority of product defects arise. The dataset from the semiconductor manufacturing process has been used for this purpose. First, it was necessary to address dataset quality. Significant multicollinearity was found in the data and to detect and delete the collinear variables, correlations and variance inflation factors have been used. The MICE-CART method has been used for the imputation because the original dataset contained more than 5% of random missing values. In further analysis, the K-means clustering method has been used to separate the failed products from the flawless ones. Following this, the hierarchical clustering method has been used for the failed product to create groups of product defects with similar properties. For the optimal number of clusters, the determination of the BIC method has been used. Five clusters of products have been made although only three can be classed as important for further analysis. These groups of products should be directly subjected to the analysis in the production process, which can assist in identifying the source of scarcity.
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