https://www.selleckchem.com/products/ly3023414.html In order to improve the machining quality and reduce the dimensional errors of micro high-aspect-ratio straight thin walls, the on-line cutting parameter compensation device has been introduced and corresponding micromilling processes have been investigated. Layered milling strategies for the micromilling of thin walls have been modeled and simulated for thin walls with different thicknesses based on the finite element method. The radial cutting parameters compensation method is adopted to compensate the thin wall deformation by raising the radial cutting parameters since the thin wall deformation make the actual radial cutting parameters smaller than nominal ones. The experimental results show that the dimensional errors of the thin wall have been significantly reduced after the radial cutting parameter compensation. The average relative dimensional error is reduced from 6.9% to 2.0%. Moreover, the fabricated thin walls keep good shape formation. The reduction of the thin wall dimensional error shows that the simulation results are reliable, which has important guiding significance for the improvement of thin wall machining quality, especially the improvement of dimensional accuracy. The experimental results show that the developed device and the machining strategy can effectively improve the micromilling quality of thin walls.Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions. In recent years, unsupervised feature selection algorithms have attracted considerable attention in the field of gene selection as they can find the most discriminating subsets of genes, namely the potential information in biological data. Recent research also shows that maintaining the important structure of data is necessary for gene selection. However, most current feature selection methods mer