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Experimental measurement and numerical simulation are two typical methods to monitor the strength variation of structures. However, the former method is difficult to lay sufficient sensors on structures with large sizes or complex curved surfaces, and the latter one suffers from low prediction accuracy due to the simplification and idealization of the physical entity. How to efficiently utilize and combine these two kinds of methods remains a challenging problem. In this study, a novel digital twin modeling method via transfer learning-based multi-source data fusion (DTM-TL-MSDF) is proposed to make full use of the experiment data and the simulation data, aiming to establish an accurate digital twin model for structural strength monitoring in real-time. In the off-line stage, the clustering algorithm is used to pre-process the huge simulation data to relieve the computational burden, and the deep neural network (DNN) model is then pre-trained using the pre-processed simulation data. In the on-line stage, the pre-trained DNN model is fine-tuned using the experimental data to carry out transfer learning. Moreover, the bagging algorithm is employed in the fine-tuning process to improve the robustness and prediction accuracy due to its ability to address the dataset with only a small number of training points. To illustrate the effectiveness of the DTM-TL-MSDF method, a one-dimensional test function and an experimental study of a rectangular plate with hole under axial tension are studied. Results indicate that the DTM-TL-MSDF method can build an accurate digital twin model by integrating the simulation data and the experimental data with excellent global and local accuracy, providing a novel solution to monitor the variations of the structural full-field strength in real-time. [ABSTRACT FROM AUTHOR]

Abstract: T cell immunoglobulin and mucin domain (Tim)-3 is expressed on activated CD4+ and CD8+ T cells. Identification of galectin-9 as a ligand for Tim-3 has now firmly established the Tim-3/galectin-9 pathway, which results in apoptosis of effector CD4+ and CD8+ T cells. Moreover, Th17 cells are a recently discovered CD4+ effector T cell, which are important in antimicrobial immunity. Whether the Tim-3/galectin-9 pathway affects Th17 immunity has not been elucidated. Here, we demonstrated expression of Tim-3 on Th17 cells by flow cytometry. Th17-skewed cells were sensitive to galectin-9-induced apoptosis. In vitro administration of galectin-9 decreased stimulated Th17 cells and inhibited production of IL-17. Interestingly, Klebsiella pneumoniae (K. pneumoniae) infection led to enhanced IL-17 levels. Recombinant galectin-9 significantly decreased IL-17 in vivo, which resulted in reduced bacterial clearance and high mortality. These observations suggest that the Tim-3/galectin-9 pathway plays an important role in termination of Th17-immune responses, and could be a therapeutic target for inflammatory diseases. [Copyright &y& Elsevier]