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Based on the mining conditions and development status of Huolinhe North Open-pit Coal Mine, in view of the problems of adjacent coal mining area steering, coal column mining in the mining section, tight of the inner dump space, the stability of composite slope of stope-inner dump is analyzed to solve the potential slope safety problems caused by the landslide area of internal drainage pressing foot, mining and timely pressing slope, determine the final mining form and mining mode of the coal pillar area in the mining section, improve the coal resource recovery rate, the mining and stripping project plan during the transition period of the mining area is planned in detail, and the inner dumping space is optimized to avoid the outer dumping area, which provides guarantee for the safe and efficient production of open-pit coal mine.

The existing algorithm for detecting the helmet wear by coal miners has the problem of difficulty in achieving a good balance between detection accuracy and speed. In order to solve the above problem, based on the YOLOv4 model, a lightweight model (M-YOLO) that integrates coordinate attention and multi-scale is proposed and applied in safety helmet wearing detection. This model replaces YOLOv4's feature extraction network CSPDarknet53 with a lightweight feature extraction network S-MobileNetV2 composed of a mixed coordinate attention module. It effectively improves the connection between features while reducing the number of related parameters. The model changes the parallel connection method in the original spatial pyramid pooling structure to serial connection. It effectively improves computational efficiency. The feature fusion network is improved by introducing shallow features with high-resolution and multi detail texture information. It effectively enhances the extraction of object features. Some convolutions in the original Neck structure are modified to deep separable convolutions, further reducing the model's parameter and computational complexity while ensuring detection precision. The experimental results show that compared with the YOLOv4 model, the mean average precision of the M-YOLO model is only reduced by 0.84%. But the computational complexity, parameter quantity, and model size are reduced by 74.5%, 72.8%, and 81.6%, respectively. The detection speed is improved by 53.4%. Compared to other models, the M-YOLO model achieves a good balance between accuracy and real-time performance, meeting the requirements of embedded loading and deployment on intelligent video surveillance terminals.

[Objective] In order to help China’s iron and steel industry to enter the carbon emission trading market, previous studies have mainly allocated carbon quotas for iron and steel enterprises from the perspective of the impact of cost. However, the iron and steel industry has large carbon emissions and complex production processes, and overemphasizing the impact of cost will easily reduce the benefits of emission reduction, which is not conducive to promoting the low-carbon transformation of iron and steel enterprises. Based on the carbon emission reduction efficiency and output efficiency, this study aimed to design a carbon quota allocation scheme that conforms to the current development situation of China’s iron and steel industry. [Methods] This study reviewed the production steps of different iron and steel production processes, took carbon quota as the necessary raw material for continuous production of each steps, and reconstructed the iron and steel production processes. By superposing carbon quota, an input-output stochastic frontier model was constructed, and the estimation formula of production efficiency and carbon quota efficiency of the iron and steel industry was obtained. Comparing the impact of different carbon quota reference values on the output rate of the iron and steel production processes, and taking efficiency improvement rate as the selection standard, this study determined the cross-regional carbon quota baselines of China’s iron and steel industry. [Results] Compared with the historical emission method, benchmarking is more suitable for the carbon quota allocation of China’s iron and steel industry. The carbon quota baselines of China’s iron and steel industry should be set in the top [30%, 50%] range of the industry, and the optimal baseline should be set in the top 40% of the industry. When the carbon emission is greater than the top 50% of the industry, the production rate of the iron and steel industry decreases with the increase of the carbon emission baseline. Incorporating the iron and steel industry into China’s carbon emission trading scheme by the benchmarking method will help this industry to both save carbon tariffs and obtain a carbon market share. [Conclusion] Allocating carbon quotas based on output efficiency can help improve the efficiency of emission reduction in the iron and steel industry, and stimulate the potential of independent emission reduction in this industry. Extending the quota allocation method of this study will help the whole process of decarbonization in China’s iron and steel industry.

This paper considers an optimal asset allocation problem for a defined contribution pension fund in a continuous time setting, and the manager is concerned about potential model misspecification. We suppose that the financial market consists of one risk-free asset, one market index fund whose price satisfies the constant elasticity of variance model, and a pair of risky assets with mispricing. Under the objective of maximizing the expected utility of the pension fund wealth at the retirement, the closed form expressions of the robust optimal investment strategy and the corresponding value function are obtained by using the stochastic control theory. Finally, some numerical examples are given to compare the difference between the ambiguity averse and the ambiguity neutral strategies, and to investigate the effect of relevant parameters on the strategy. We find that the optimal investment strategy is not a long-short symmetric strategy when the mispricing assets have different mispricing correct capability, and it will lead to significant utility loss for the manager if the model uncertainty risk is ignored. [ABSTRACT FROM AUTHOR]

Since February 2020, COVID-19 has spread rapidly to more than 200 countries in the world. During the pandemic, local governments in China have implemented different interventions to efficiently control the spread of the epidemic. Characterizing transmission of COVID-19 under some typical interventions is essential to help countries develop appropriate interventions. Based on the pre-symptomatic transmission patterns of COVID-19, we established a novel compartmental model: Susceptible-Infectious-Confirmed-Removed (SICR) model, which allowed the effective reproduction number to change over time, thus the effects of policies could be reasonably estimated. Using the epidemic data of Wuhan, Wenzhou, and Shenzhen, we migrated the corresponding estimated policy modes to South Korea, Italy, and the United States and simulated the potential outcomes for these countries when they adopted similar policy strategies to China. We found that the mild interventions implemented in Shenzhen were effective in controlling the epidemic in the early stage, while more stringent policies which were implemented in Wuhan and Wenzhou were necessary if the epidemic became severe and needed to be controlled in a short time. [ABSTRACT FROM AUTHOR]