The field of numerical methods and matrix computations is witnessing significant developments, with a focus on improving the efficiency and accuracy of various algorithms. Researchers are exploring new approaches to solve complex problems, such as the Yang-Baxter-like matrix equation and the retrieval of top-k elements from factorized tensors. The use of neural networks and machine learning techniques is also becoming increasingly popular in this area, with applications in nonlinear solvers and preconditioned Newton methods. Additionally, there is a growing interest in the development of fast direct solvers and efficient numerical evaluation methods for various mathematical problems, including fractional Laplacian models and triple integrals. Noteworthy papers in this area include: A Neural-Operator Preconditioned Newton Method for Accelerated Nonlinear Solvers, which proposes a novel neural preconditioned Newton method for solving parametric nonlinear systems of equations. A Novel Block-Alternating Iterative Algorithm for Retrieving Top-k Elements from Factorized Tensors, which develops a block-alternating iterative algorithm for retrieving the k largest or smallest elements from a low-rank tensor. Fast Direct Solvers, which describes a class of methods known as fast direct solvers for solving systems of linear equations arising from the discretization of elliptic PDEs or integral equations.