Abstract: The disclosure belongs to the technical field of well completion engineering in oil and gas engineering, and particularly relates to a high-flow-conductivity proppant and its preparation method. The high-flow-conductivity proppant includes dumbbell-shaped framework granules and an oil-soluble film coating layer coated on the surfaces of the framework granules; the oil-soluble film coating layer melts when meeting oil at the temperature of 30-120° C. The high-flow-conductivity proppant prepared by the method has low bulk density, and the problems that the traditional proppant cannot be transported to the far end of a crack, has poor flow conductivity and the like can be effectively solved.

Abstract: A joint sealing system for prefabricated building components is provided. The joint sealing system includes a first component including a first surface, and a second component including a second surface that is mateable with the first surface. The first and second surfaces define a joint. A sealant composition is disposed on one of the first surface or the second surface. Pressure exerted when the second surface is mated with the first surface triggers curing of the sealant composition to join the first and second components and to seal the joint between the first and second components. A method of joining and sealing two prefabricated building components is also provided.

Abstract: An aluminum plastic film of a battery, includes a pit and a flanging. The pit is used for placing a battery core and has a bottom surface that is connected to a first side elevation and a second side elevation. The first side elevation and the second side elevation are connected to each other. A first lower arc chamfer is formed at the joint of the bottom surface and the first side elevation, and a second lower arc chamfer is formed at the joint of the bottom surface and the second side elevation. The flanging is located at the upper periphery rim of the pit and connected to the upper end of the side elevation. A first upper arc chamfer and a second upper arc chamfer are formed at the joints of the first side elevation and the flanging and of the second side elevation and the flanging.

Abstract: A joint sealing system for prefabricated building components is provided. The joint sealing system includes a first component including a first surface, and a second component including a second surface that is mateable with the first surface. The first and second surfaces define a joint. A sealant composition is disposed on one of the first surface or the second surface. Pressure exerted when the second surface is mated with the first surface triggers curing of the sealant composition to join the first and second components and to seal the joint between the first and second components. A method of joining and sealing two prefabricated building components is also provided.

Abstract: A noise detection method for time-series vegetation index (TSVI) derived from remote sensing images. Firstly, unit root test is used to classify observation values of each pixel into a stationary series or a non-stationary series; for the non-stationary, an appropriate mathematical model is used to model discrete TSVI, then differences between actual observation values and prediction values of the model are calculated and recorded as a deviation. As the deviation has removed seasonal components, the non-stationary series is transformed into a stationary series. For a stationary series or deviation data, noise detection is performed based on the assumption that observation values are distributed within a certain range around mean values; then model fitting and noise detection are iteratively carried out with remained observation values—until the iterations reached the maximum number or no noise is detected at one iteration.

Abstract: A noise detection method for time-series vegetation index (TSVI) derived from remote sensing images. Firstly, unit root test is used to classify observation values of each pixel into a stationary series or a non-stationary series; for the non-stationary, an appropriate mathematical model is used to model discrete TSVI, then differences between actual observation values and prediction values of the model are calculated and recorded as a deviation. As the deviation has removed seasonal components, the non-stationary series is transformed into a stationary series. For a stationary series or deviation data, noise detection is performed based on the assumption that observation values are distributed within a certain range around mean values; then model fitting and noise detection are iteratively carried out with remained observation values—until the iterations reached the maximum number or no noise is detected at one iteration.