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First, gravity is a force that causes objects to attract one another. The simplest way to understand gravity is through Isaac Newton's law of universal gravitation. This law states that every particle in the universe attracts every other particle. The more massive an object is, the more strongly it attracts other objects. The closer objects are, the more strongly they attract each other. An enormous object, like the Earth, easily attracts objects that are close to it, like apples hanging from trees. Scientists haven't decided exactly what causes this attraction, but they believe it exists everywhere in the universe. Second, air is a fluid that behaves essentially the same way liquids do. Like liquids, air is made of microscopic particles that move in relation to one another. Air also moves like water does -- in fact, some aerodynamic tests take place underwater instead of in the air. The particles in gasses, like the ones that make up air, are simply farther apart and move faster than the particles in liquids. The first step is to construct the salient feature extraction module. The convolutional block attention module (CBAM) based on SPCNN to perform saliency detection. Then a SPherical Convolutional Gated Recursive Unit (SP-ConvGRU) is used, which is a time-series model of FoV information from the FoV feedback from a small number of users. Finally, salient spatial-temporal video features and historical FoV information are combined for accurate FoV prediction. Experimental results show that the performance of the proposed method is better than other prediction methods. Presenting the special FoV prediction problem in a 360-degree video multicast scenario and designing a prediction method with video saliency and a limited number of real users’ FoV information. Using SPCNN to eliminate the projection distortion of 360-degree video, and proposing a saliency detection model based on SPCNN to extract spatial-temporal features from 360-degree video and introduce an attention mechanism in the network to improve the performance. Performing exhaustive experiments to show that the proposed method achieves better results than other methods on publicly available 360-degree video datasets.|Transient thermal analysis determines temperatures. Other thermal quantities which vary over time. In many instances, the temperatures from a transient thermal analysis are utilized as remarks for a structural analysis, which is used for thermal stress evaluations. The temperature distribution's variation over time is useful in many practical applications such as with a quenching analysis for heat treatment or with cooling of electronic packages. Several heat transfer applications such as electronic package design, heat treatment problems, nozzles, fluid-structure interaction problems, pressure vessels, engine blocks, etc. involve transient thermal analysis. A transient thermal assessment can be either linear or nonlinear. Temperature dependent material properties such as thermal conductivity, specific heat or density, or radiation effects or temperature dependent convection coefficients can produce in nonlinear analysis which requires a reiterative procedure to reach accurate solutions. Most materials' thermal properties vary with temperature. Hence the analysis generally is nonlinear. Specific heat, density and thermal conductivity need to be defined for transient thermal assessments. 2

















































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