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Several di-nitrogen Schiff bases were synthesized through the condensation of 2-pyridinecarboxaldehyde with primary amines. The Schiff bases as ligands coordinated with methyltrioxorhenium (MTO) smoothly to afford the correspondent complexes which were characterized by IR, 1 H NMR, 13 C NMR, MS and elemental analysis. One of the complexes was analyzed by X-ray crystallography as well. The results revealed that the complexes display distorted octahedral geometry in the solid state with a trans-position of Schiff base. Catalytic results indicated that the complexes as catalysts increased the selectivity of epoxides remarkably compared with MTO in the epoxidation of alkenes with 30% hydrogen peroxide as oxidant and the increasing rate depended on the structure of the Schiff base ligands of the complexes. The results indicated that the stronger the donating ability of the ligand, the higher selectivity of epoxides the complex gave in the epoxidation of alkenes with 30% hydrogen peroxide as oxidant.

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With the rise of deep learning technology, salient object detection algorithms based on convolutional neural networks (CNNs) are gradually replacing traditional methods. The majority of existing studies, however, focused on the integration of multi-scale features, thereby ignoring the characteristics of other significant features. To address this problem, we fully utilized the features to alleviate redundancy. In this paper, a novel CNN named local and global feature aggregation-aware network (LGFAN) has been proposed. It is a combination of the visual geometry group backbone for feature extraction, an attention module for high-quality feature filtering, and an aggregation module with a mechanism for rich salient features to ease the dilution process on the top-down pathway. Experimental results on five public datasets demonstrated that the proposed method improves computational efficiency while maintaining favorable performance.