Influence of Particle Distribution on Macroscopic Properties of Particle Flow Model

The particle flow discrete element models for uniaxial compression and tensile tests of rocks are established to study the influence of the particle distribution randomness on the macroscopic mechanical properties of such model. The results of macroscopic mechanical properties show strong discreteness due to the variance of particle distributions, in which compressive strength, tensile strength and Poisson's ratio follow the normal distribution and the Young's modulus follows the negative skewness distribution. The average values of the macroscopic strength obtained based on multiple calculations with different particle distributions should be used for the calibration of microscopic parameters. According to the relationship between sample size and deviation of macro strength averages, the minimum calculation number required to obtain high-precision macro strength with different confidence levels is given.

Response of Population Canopy Color Gradation Skewed Distribution Parameters of the RGB model to Micrometeorology Environment in Begonia Fimbristipula Hance

Background: The high quality and efficient production of greenhouse vegetation depend on the micrometeorology environmental adjusting such as the system warming, illumination supplement. In order to improve the quantity, quality and efficiency of greenhouse vegetation, it is necessary to figure out the relationship between the crop growth conditions and environmental meteorological factors, which could give constructive suggestions for precise control of greenhouse environment and reducing the running cost. The parameters from the color information of plant canopy reflect the internal physiological conditions, thus, RGB model has been widely used in the color analysis of digital pictures of leaves.Results: The color scale for single leaf, single plant, and the populate canopy of Begonia Fimbristipula Hance (BFH) photographs are all have a skewed cumulative distribution histograms. The color gradation skewness-distribution (CGSD) parameters of the RGB model were increased from 4 to 20 after the skewness analysis, which greatly expanded the canopy leaf color information and could simultaneously describe the depth and distribution characteristics of canopy color. The 20 CGSD parameters were sensitive to the micrometeorology factors, especially to the radiation and temperature accumulation. The multiple regression models of mean, median, mode and kurtosis parameters to microclimate factors were established, and the spatial models of skewness parameters were optimized.Conclusions: The models constructed based on the color gradation skewness-distribution (CGSD) parameters of the RGB model, can well explain the response of canopy color to microclimate factors and can be used to monitor the variation of plant canopy color under different micrometeorology.

Signatures of the Galactic bar in high-order moments of proper motions measured by Gaia

Our location in the Milky Way provides an exceptional opportunity to gain insight on the galactic evolution processes, and complement the information inferred from observations of external galaxies. Since the Milky Way is a barred galaxy, the study of motions of individual stars in the bulge and disc is useful to understand the role of the bar. The Gaia mission enables such study by providing the most precise parallaxes and proper motions to date. In this theoretical work, we explore the effects of the bar on the distribution of higher-order moments – the skewness and kurtosis – of the proper motions by confronting two simulated galaxies, one with a bar and one nearly axisymmetric, with observations from the latest Gaia data release (Gaia DR2). We introduce the code ASGAIA to account for observational errors of Gaia in the kinematical structures predicted by the numerical models. As a result, we find clear imprints of the bar in the skewness distribution of the longitudinal proper motion μℓ in Gaia DR2, as well as other features predicted for the next Gaia data releases.