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Therefore, the simplified and efficient three-dimensional reconstructed surface is achieved based on the real machining surface. By analyzing a three-dimensional reconstructed surface finite element model, it is determined that the surface roughness has a more significant impact on the contact performance parameters compared with the various machining methods. The contact deformation increases with the increase of surface roughness in https:\/\/wizardsdev.com\/en\/news\/<\/a> the same case, while the curves of average contact stress, contact stiffness, and contact area have the opposite trend.<\/p>\n<\/p>\n We illustrated the features of our model by integrating the HLCA. ScPoli outperformed the model used in the original study in data integration and yielded a sample latent representation that reflected similarities between different samples. We also showcased our model\u2019s reference mapping capabilities by mapping an unlabeled query dataset of healthy samples and one of cancer samples. Furthermore, we demonstrated the viability of scPoli as a scATAC-seq data integration method and its ability to perform integration and reference mapping across species. After building a reference using the HLCA core dataset, we mapped a group of healthy samples (Meyer, 2021)32 (Fig. 3f). These data consist of six samples and contain nine cell identities not present in the reference.<\/p>\n<\/p>\n<\/p>\n
Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations<\/h2>\n<\/p>\n