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BackgroundSince the global epidemic of the coronavirus disease 2019 (COVID-19), a large number of immunological studies related to COVID-19 have been published in various immunology journals. However, the results from these studies were discrete, and no study summarized the important immunological information about COVID-19 released by these immunology journals. This study aimed to comprehensively summarize the knowledge structure and research hotspots of COVID-19 published in major immunology journals through bibliometrics.MethodsPublications on COVID-19 in major immunology journals were obtained from the Web of Science Core Collection. CiteSpace, VOSviewer, and R-bibliometrix were comprehensively used for bibliometric and visual analysis.Results1,331 and 5,000 publications of 10 journals with high impact factors and 10 journals with the most papers were included, respectively. The USA, China, England, and Italy made the most significant contributions to these papers. University College London, National Institute of Allergy and Infectious Diseases, Harvard Medical School, University California San Diego, and University of Pennsylvania played a central role in international cooperation in the immunology research field of COVID-19. Yuen Kwok Yung was the most important author in terms of the number of publications and citations, and the H-index. CLINICAL INFECTIOUS DISEASES and FRONTIERS IN IMMUNOLOGY were the most essential immunology journals. These immunology journals mostly focused on the following topics: “Delta/Omicron variants”, “cytokine storm”, “neutralization/neutralizing antibody”, “T cell”, “BNT162b2”, “mRNA vaccine”, “vaccine effectiveness/safety”, and “long COVID”.ConclusionThis study systematically uncovered a holistic picture of the current research on COVID-19 published in major immunology journals from the perspective of bibliometrics, which will provide a reference for future research in this field.

The fission nuclear reactor is a typical complicated multi-physics coupling system because of the nonlinear coupled terms among different physical fields. The Newton-Krylov method is an effective method for solving the multi-physics coupling problem, featuring strong stability and a high-order convergence rate. Recently, the Newton-Krylov method with an explicit Jacobian matrix has become popular. Compared with the JFNK (Jacobian-free Newton-Krylov) method which doesn’t form the Jacobian matrix explicitly, it has a better preconditioner matrix (the Jacobian matrix itself) and can achieve a more stable and fast convergence. How to calculate the Jacobian matrix efficiently and automatically is a major challenge. The finite difference method is an effective way to compute the Jacobian matrix automatically and can avoid the derivation of matrix element expressions. Besides, the serial graph coloring algorithm has been utilized to achieve an efficient computation of Jacobian. By exploiting the sparsity of the Jacobian matrix, all structurally orthogonal columns can be computed simultaneously through one function evaluation. Thus, the Jacobian computational cost can be reduced by one order of magnitude. However, when solving the larger scale problem under a distributed-memory parallel environment, the parallel graph coloring algorithm is required because the Jacobian is distributed among all processors. In this study, a Distance-2 graph coloring algorithm, arising from the field of graph theory, is applied to color the Jacobian matrix in parallel. This algorithm is performed iteratively, with each iteration consisting of four stages. Local coloring: Each processor tentatively colors its diagonal submatrix using greedy coloring algorithms; Color transfer: The colors of the diagonal submatrix are transferred to other processors to update the off-diagonal submatrix. Conflict detection: Each processor concurrently checks whether the color of the off-diagonal submatrix conflicts with the diagonal submatrix. The conflicts are gathered back to the corresponding diagonal submatrix. Color reset: Reset columns with color conflicts, waiting for recoloring in the next iteration. The entire iteration process continues until each column is correctly colored. This parallel coloring algorithm was verified by solving the steady and transient neutronic/thermal-hydraulics coupling problem arising from a simplified pressurized water reactor model. The Jacobian coloring and Jacobian computing based on this method were tested under a different number of processors. The results indicate that the Jacobian matrix computed by this parallel coloring algorithm is completely correct. The parallel coloring number only slightly increases with the number of processors. The parallel Newton-Krylov method with explicit Jacobian is more efficient and stable than the parallel JFNK method. In a word, this parallel coloring algorithm is successfully applied to the parallel Newton-Krylov method for solving the large-scale neutronic/thermal-hydraulics coupling problem.

Focuses on the value of advertising by food manufacturers as investment in intangible capital in the United States. Concept of intangible capital; Role of consumer product innovation in competition in the food industry; Inclusion of brand equity and research and development as intangible capital.