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<p>On the basis of logistic models of forest restoration, we consider the influence of population pressure on forest restoration and establish a reaction diffusion model with Holling Ⅱ functional responses. We study this reaction diffusion model under Dirichlet boundary conditions and obtain a positive equilibrium. In the square region, we analyze the existence of Turing instability and Hopf bifurcation near this point. The square patterns and mixed patterns are obtained when steady-state bifurcation occurs, the hyperhexagonal patterns appears in Hopf bifurcation.</p>
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Deep learning has been widely used in various fields and showed promise in recent years. Therefore, deep learning is the future trend to realize seismic data’s intelligent and automatic interpretation. However, traditional deep learning only uses labeled data to train the model, and thus, does not utilize a large amount of unlabeled data. Self-supervised learning, widely used in Natural Language Processing (NLP) and computer vision, is an effective method of learning information from unlabeled data. Thus, a pretext task is designed with reference to Masked Autoencoders (MAE) to realize self-supervised pre-training of unlabeled seismic data. After pre-training, we fine-tune the model to the downstream task. Experiments show that the model can effectively extract information from unlabeled data through the pretext task, and the pre-trained model has better performance in downstream tasks.
Distributed beamforming uses nodes in a wireless sensor network to transmit signals in different phases with controllable delay, to obtain coherent output signals with a gain after superposition. However, a wireless sensor network has a large topological area and wide distribution range, and it is difficult for distributed beamforming to obtain a highly directional beam as centralized beamforming does, which will cause interference to the non-target base stations. To solve this problem, a discrete adaptive dual-population cooperative differential evolution (DPCDE) algorithm is proposed, which can effectively reduce the interference by selecting nodes suitable for participating in distributed beamforming in a wireless sensor network. Simulation results show that the proposed algorithm can optimize the node set participating in distributed beamforming to minimize the interference of the wireless sensor network to the non-target base stations, and the effect is better than other classic intelligent optimization algorithms.
Liquid vaporization under thermodynamic phase non-equilibrium condition at the gas-liquid interface is investigated over a wide range of fluid state typical of many liquid-fueled energy conversion systems. The validity of the phase-equilibrium assumption commonly used in the existing study of liquid vaporization is examined using molecular dynamics theories. The interfacial mass flow rates on both sides of the liquid surface are compared to the net vaporization rate through an order-of-magnitude analysis. Results indicated that the phase-equilibrium assumption holds valid at relatively high pressures and low temperatures, and for droplets with relatively large initial diameters (for example, larger than 10 µm for vaporizing oxygen droplets in gaseous hydrogen in the pressure range from 10 atm to the oxygen critical state). Droplet vaporization under superheated conditions is also explored using classical binary homogeneous nucleation theory, in conjunction with a real-fluid equation of state. It is found that the bubble nucleation rate is very sensitive to changes in saturation ratio and pressure; it increases by several orders of magnitude when either the saturation ratio or the pressure is slightly increased. The kinetic limit of saturation ratio decreases with increasing pressure, leading to reduced difference between saturation and superheat conditions. As a result, the influence of nonequilibrium conditions on droplet vaporization is lower at a higher pressure.
Particleboard is a widely used panel material because of its physical properties and low cost, the hot-pressing operation is the one of the most important stages of the whole particleboard production in which many physical processes are involved, such as mat moisture content and distribution, heat transfer. A number of factors are involved including resin type, press temperature, wood species and press closing time, pressure during pressing, Considering coupled heat transfer with resin release ,we presented a mathematical model have been given and solve the modeling with the method of variable separation characteristics and function expansion method. The model has described the relation of center layer temperature changes with pressing times. It is helpful for further research of Internal change in hot-pressing
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