Compacted Sludge Reducing Barrier (CSRB) was tested to be a feasible barrier for Acid Mine Drainage (AMD) from tailings in case the ground water contamination. Because of its double function (encapsuling and reducing), the microbial community structure diversity in the sludge played a key role. In this paper, we researched the correlation between heavy metals and microbial structure diversity in the dewatered sludge from 4 different sewage treatment process plants in Lanzhou city, a developing area of northwest China, in the colder season. The results indicated that the microbial community structure diversity differed and were unique among the different sewage plants; their correlation with heavy metals was also unique independently.

Mining cranes play an important role in the transportation and installation of underground equipment. Operating swing angles will seriously affect the operations safety. To ensure the operation safety of mining cranes, this paper firstly established a mathematical model of mining crane hoisting swing, and discussed affecting factors of the oscillation. Then, the orthogonal test method has been carried out, and the optimal and serious working conditions were obtained. Finally, the accuracy of the numerical results have been cross-verified on the ADAMS virtual platform. Results show that the swing trend increases with the increase of pitch acceleration, rotary acceleration and rope length, especially the rotary angular acceleration. The vibration variation trends of Matlab numerical results and ADAMS simulation results are consistent, which cross-verifies the two extreme operation conditions are valid and reliable. It provides a useful reference for the future research.

China has a large electricity consumption with a large proportion of people living in the country, and the electricity consumption and generation in diverse regions are not balanced. The forecast of electricity load and short-term energy consumption can be helpful in easing the long-term electricity consumption shortage. To optimize the power dispatching strategy of each province and city, we must pay attention to the short-term forecast of regional power load. In this paper, a new short-term power load forecasting HP-ARIMA-BP model is used to screen and count relevant power data from all over the country. Use LSTM to fill in the missing values of time series, with HP filter decomposition to decompose electricity consumption in Anhui Province, remove trend items and volatility interference in the data, apply ARIMA to predict the trend items, and use BP neural network to predict the volatility items. The final fit achieves high accuracy.