Focusing on axial spondyloarthritis, the review covers essential topics like epidemiology, pathogenesis, and clinical features, while outlining various disease subsets. It includes diagnostic guides and treatment algorithms, supplemented by demonstrative images to enhance understanding. Current and emerging treatment options are also discussed, providing a comprehensive resource for healthcare professionals.
A Nonlinear Model for Numerical Function Reconstruction from Discrete Data Sets in Higher Dimensions
The book introduces a Regularization Network approach utilizing Kolmogorov's superposition theorem to reconstruct higher-dimensional continuous functions from discrete data points. It presents a new constructive proof of the theorem and explores its various versions, linking them to well-known approximation methods and Neural Networks. The work addresses the challenge of the curse of dimensionality, proposing a nonlinear model for function reconstruction within a reproducing kernel Hilbert space. It includes verification and analysis through numerous numerical examples.