Search

In order to solve the problem that a large amount of gas emitted from the newly excavated coal wall in the driving roadway after the sudden wind shutdown causes the gas volume fraction to rise rapidly and thus endangers the safety of the operators, this paper proposes a mathematical model for the space-time evolution of the gas volume fraction in the driving roadway during the wind shutdown, and uses the multi-physical field coupling software to simulate and study the space-time evolution law of the gas emission, gas volume fraction, and oxygen volume fraction in the driving roadway after the sudden wind shutdown accident. The results show that the gas diffusion in the roadway after the ventilation is low Reynolds number laminar flow; the comparison and analysis trend of theoretical calculation data is consistent with the measured data of 2207 heading roadway in Taiyue Coal Mine; the escape speed of the personnel involved in the danger must be greater than 3.91 m/min, the best evacuation time is 7-25 min, and the minimum time is 30 min before the oxygen volume fraction in the roadway falls to the low oxygen level. The research results provide a theoretical basis for formulating the emergency response plan for coal mine gas safety accidents and scientifically arranging the emergency evacuation of operators.

The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite.