Structural analysis of floating pipes of the fish cage in currents

A numerical model is developed to investigate the structural performance and stress distribution of floating pipes of fish cage subjected to the flow. The modeling approach is based on the joint use of the finite element method using the shell elements to simulate the floating pipes and the hydrodynamic force model improved from the Morison’s equation and lumped-mass method. The hydrodynamic response of the fish cage and forces on the floating pipes can be obtained by the Morison’s equation and lumped-mass method. The stress and deformation of the floating pipes can be evaluated using the finite element method. Using an appropriate iterative scheme, the stress distribution and maximum stress of the floating pipes can be obtained using the proposed model. To validate the numerical model, the numerical results were compared with the data obtained from corresponding physical model tests. The comparisons show the numerical results agree well with the experimental data. On that basis, the simulations of floating pipes in currents are performed to investigate the maximum stress and the critical locations. Simulations of the fish cage in different flow velocity are performed. The effect of the velocity on the deformations and stress of the floating pipes is analyzed. The simulations results show that the stress and deformations drastically increases with the increase of flow velocity. Comparing results of floating pipes with different mooring line arrangements indicates that increasing mooring lines can efficiently lower the stress of the floating pipes. The simulation of the SPM cage system with multiple net cages in current is preformed and the results show the middle cage is most dangerous for the tripartite-cage system

Decision analysis of slope ecological restoration based on AHP

The serious deterioration of the ecological environment comes from a large number of geological disasters. These disasters were caused by a number of engineering activities. Ecological restoration is an important measure to reduce geological disasters and protect the ecological environment. On the basis of the introduction of cast-in-situ grids technology, external-soil spray seeding technology and vegetation bag technology, according to the ecological restoration experiment of the road slope attach to the Three Gorges Pumped-Storage Power Station in Hohhot, decision analysis of slope ecological restoration is done with AHP. It is shown that in arid and semi-arid area, selection of slope ecological restoration scheme mainly needs considering the ecological effect and stability. The major factor of ecological effects is survival rate of vegetation. The major factor of stability is the stability in a whole. Cast-in-situ grids technology will be the first choice for ecological restoration of road slope in arid and semi-arid area. This study provides reference for decision of the slope ecological restoration in arid and semi-arid region

FFA-Net: Feature Fusion Attention Network for Single Image Dehazing

In this paper, we propose an end-to-end feature fusion at-tention network (FFA-Net) to directly restore the haze-free image. The FFA-Net architecture consists of three key components: 1) A novel Feature Attention (FA) module combines Channel Attention with Pixel Attention mechanism, considering that different channel-wise features contain totally different weighted information and haze distribution is uneven on the different image pixels. FA treats different features and pixels unequally, which provides additional flexibility in dealing with different types of information, expanding the representational ability of CNNs. 2) A basic block structure consists of Local Residual Learning and Feature Attention, Local Residual Learning allowing the less important information such as thin haze region or low-frequency to be bypassed through multiple local residual connections, let main network architecture focus on more effective information. 3) An Attention-based different levels Feature Fusion (FFA) structure, the feature weights are adaptively learned from the Feature Attention (FA) module, giving more weight to important features. This structure can also retain the information of shallow layers and pass it into deep layers. The experimental results demonstrate that our proposed FFA-Net surpasses previous state-of-the-art single image dehazing methods by a very large margin both quantitatively and qualitatively, boosting the best published PSNR metric from 30.23db to 36.39db on the SOTS indoor test dataset. Code has been made available at GitHub.Comment: Accepted by AAAI202

Spatiotemporal features of microsporogenesis in the cycad species Macrozamia communis

Plant Biology, Ecology and Evolutio