Bioturbation of infauna plays an important role in the biogeochemical processing of sediments. Infaunal animals build burrows and enlarge the sediment-water interface by their activities and so bioturbation is closely related with burrow structure and animal behavior in the sediment. The purpose of this study is to explore the characteristics of Perinereis aibuhitensis burrow structures with the factors of months and animal sizes (0-1g, 1-2g, 2-3g, 3-4g, and >4g), which would also provide useful knowledge of infauna behavioral ecology. The dimension and complexity of the burrows of P. aibuhitensis were measured by dissecting sediments. The results showed that there were three burrow shapes of P. aibuhitensis, i.e., I, Y and U shapes. Overall, the order of abundance of each of the three burrow shapes were I > Y > U. Larger P. aibuhitensis are inclined to build Y- and U-shaped burrows in June and August. There were significant differences in the tunnel diameter, burrow depth and burrow length separately between different polychaete size classes (P< 0.001). In February and August, the burrow depths and burrow lengths of P. aibuhitensis individuals with body weights of 1-2 g and 2-3 g were significantly greater than in other months (P< 0.001). P. aibuhitensis individuals of 1-2 g and 3-4 g body weight had significantly more burrow openings and branches in August than in February (P< 0.001). Within the same month, the burrow HEindex increased with increasing polychaete size, and when the sizes were 1-2 g, 2-3 g and 3-4 g, the complexity in August was higher than that in other months. This study suggests that I-shaped burrow dominants the burrow architecture of P. aibuhitensis. The polychaete with large size has a higher HEindex (burrow complexity) indicating a strong bioturbation ability. Y-shaped burrows are more conducive to the survival of P. aibuhitensis in hot weather. In order to adapt to environmental stresses outside, P. aibuhitensis usually builds deeper burrows.
With the increase of the output of electric vehicles, it is of great significance to predict the health status of lithium-ion batteries for the safe operation of electric vehicles. In this paper, some common data-driven methods for health state estimation of lithium-ion batteries are reviewed. First of all, this paper introduces the charge and discharge principle of lithium-ion battery. Then four common SOH prediction methods are introduced, and their advantages and disadvantages are summarized and reviewed. In the part of introducing the data-driven research on the health status of lithium-ion battery, it focuses on the application of machine learning and deep neural network. Finally, the research prospect of health state estimation of lithium-ion battery is explained.
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