Tigers and their close relatives (Panthera) are some of the world’s most endangered species. Here we report the de novo assembly of an Amur tiger whole-genome sequence as well as the genomic sequences of a white Bengal tiger, African lion, white African lion and snow leopard. Through comparative genetic analyses of these genomes, we find genetic signatures that may reflect molecular adaptations consistent with the big cats’ hypercarnivorous diet and muscle strength. We report a snow leopard-specific genetic determinant in EGLN1 (Met39>Lys39), which is likely to be associated with adaptation to high altitude. We also detect a TYR260G>A mutation likely responsible for the white lion coat colour. Tiger and cat genomes show similar repeat composition and an appreciably conserved synteny. Genomic data from the five big cats provide an invaluable resource for resolving easily identifiable phenotypes evident in very close, but distinct, species.

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A ship’s wind energy utilization device with multi-mode arc-shaped sails is designed, which have different working modes for sail-assisting or wind power generation according to the ship’s navigation. The structural characteristics and working principles of this device are firstly described in this paper. Three sets of arc-shaped sails with different thickness (4.5 cm, 11.3 cm, 21.7 cm) were designed. Wind tunnel experiments were carried out in the respects of sail-assisting performance and wind-power generation to determine the best sail blade shape and to verify the energy-saving effect of this device. Experiments show that the sail with the smallest thickness (4.5 cm) has a better boosting effect than others, and the sail with the largest thickness (21.7 cm) has the best wind power generation performance. Considering the lateral force and the structural strength of the support, in the case of the comprehensive evaluation for the boosting and power generation performance, it is considered that the intermediate thickness (11.3 cm) is the best choice. The device has a good comprehensive energy utilization effect and has development and application value.

Nuclear power plant operating data are characterized by a large variety, strong coupling, and low data value density. When using machine learning techniques for fault diagnosis and other related research, feature selection enables dimensionality reduction while maintaining the physical meaning of the original features, thus improving the computational efficiency and generalization ability of the learning model. In this paper, a correlation-based feature selection algorithm is developed to implement feature selection of nuclear power plant operating data. The proposed algorithm is verified by experiments and compared with traditional correlation-based feature selection algorithms. The experiments and comparison results show that the proposed algorithm is effective in realizing the dimensionality reduction of nuclear power plant operating data.