With the development of society, carbon emissions are increasing. The key organisms to maintain the stability of the carbon cycle are fungi that can be easily seen and ignored. In this paper, we selected several fungi to establish the model of decomposition and reproduction so that we can understand the role they played. First of all, we studied several physiological indexes of fungi, and established the degradation model through multiple regression analysis, and multiple linear regression equation for the relationship between decomposition rate, growth rate, unit volume density of mycelium, temperature and humidity tolerance. Next, we established the competitive growth model based on logistic model, simulated the competitive growth process of strains with different growth rates, humidity tolerance, and the total decomposition rate. In order to be closer to the real situation, we set up the competitive growth model among four species. By arranging fungal communities randomly to simulate different biodiversity, we analyzed the effects on the decomposition rate in the case of that the environmental temperature and humidity changed by 10% respectively. After that, we established a growth prediction model based on ARIMA. By querying the climate data of five typical climates, we established the competitive growth model with 4 combinations, and we obtained a short-term model, a medium-term trend and a long-tern forecast to describe growth, reproduction and decomposition rate. In order to refine the strains of the pressure of competition and the influence of the distance between the strains of competition, we have established improved competition evolution model based on the cellular automata theory of population. The model helped us comprehend the competition between species on a micro level. All these analyses showed us the significance of biodiversity and the great role decomposers play in Earth.
Wind velocity has an important influence on the ballistic characteristics of uncontrolled projectiles. It is difficult to precisely determine the projectile’s impact position if wind velocity information from the projectile’s flight process cannot be collected. A wind velocity identification technique of spinning projectile based on the multiobjective chaotic adaptive differential evolution algorithm is suggested to increase the estimation accuracy of wind velocity and ballistic prediction accuracy during projectile flight. The variation law of projectile aerodynamic characteristics under no wind situation is calculated using the 4D kinematic model of spinning projectile. Three aerodynamic parameter coefficients are chosen as reference variables, three objective functions are defined by mean square error, and the two components of wind velocity along the ground coordinate system are used as decision variables in the identification process. The study identifies the two components, which is based on the multiobjective chaotic adaptive differential evolution algorithm. Several groups of wind velocity identification under constant and variable wind conditions are numerically simulated. The results show that the suggested method can estimate wind velocity effectively and precisely throughout the flight of spinning projectile.
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