The present paper reports the influence of post-weld heat treatment (PWHT) on microstructure and properties of electron beam welded dissimilar joint. Ti2AlNb and TC11 alloys were used to fabricate the joints. Three PWHTs were applied to the welded joints. The structures were analysed using optical microscopy, X-ray diffraction, scanning electron microscopy and transmission electron microscopy techniques. The results show that weld metal of the fusion zone is mainly composed of α2 and β phases. As the energy input increases under different PWHTs, the decomposition degree of metastable phases ( α′/ β) rises, but the tensile strength and impact toughness of the joint reduce. Under each condition, the tensile strength of the joint is higher than that of the TC11 base metal.

The present paper reports the influence of hot working conditions on the microstructure of Ti2AlNb/TC11 dissimilar joint. Linear friction welding technique was used to fabricate the joints. The microhardness and tensile properties of the joints have been tested. It was found that the fine structure of linear friction welds underwent abnormal grain growth and abnormal grain boundary phase growth in the post-weld solution heat treated condition. This phenomenon significantly deteriorated the ductility of the joint. After appropriate hot work, abnormal big grains/phases disappeared, and the joint exhibited good tensile properties due to its fine structures.

The three-dimensional genome structure plays a key role in cellular function and gene regulation. Singlecell Hi-C technology can capture genome structure information at the cell level, which provides the opportunity to study how genome structure varies among different cell types. However, few methods are well designed for single-cell Hi-C clustering, because of high sparsity, noise and heterogeneity of single-cell Hi-C data. In this manuscript, we propose a novel framework, named ScHiC-Rep, for singlecell Hi-C data representation and clustering. ScHiC-Rep mainly contains two parts: data imputation and feature extraction. In the imputation part, a novel imputation workflow is proposed, including graph convolution-based, random walk with restart-based and genomic neighbor-based imputation. In the feature extraction part, a two-phase feature extraction method is proposed, including linear phase for chromosome level and non-linear phase for cell level feature extraction. The evaluation results show that the proposed framework outperforms existing state-of-the-art approaches on both human and mouse datasets.

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