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Evaluation of tribological behavior of tribopairs in water hydraulic radial piston pumps with multiple coatings and lubrication

Li Ran, Wei Wenshu, Liu Hao, Geng Xiangyu, Li Jianfeng, Lai Yuehua, Chen Rongming, Li Dongze, Yu Tianliang, Wang Dalong, Ye Jian, Li Shoubin, Wang Wei, Wu Huigang

Journal of Theoretical and Applied Mechanics

2024

Vol. 62 nr 4

841--854

In order to explore the tribological behavior of slipper/eccentric cam tribopairs under different lubrication conditions at power ends of water hydraulic radial piston pumps, friction-wear tests were carried out. It is found that tribopairs under water-based lubrication exhibited better friction-reducing performances than those under water lubrication alone. The average steady-state friction coefficient under water-based lubrication was between 0.17 and 0.23. HMn62-3-3-0.7/17-4PH tribopair using water-based lubrication exhibited the lowest wear rate of 1.1 · 10-12 mm3/N·m. The findings of this study offer a reference for the identification of potential material pairs for slipper/eccentric cam tribopairs.

Pollution status and health risk assessment of a specific organophos-phate pesticide-contaminated site

Jin Zhibo, Chun-Yan Diao, Li Jianfeng

Environment Protection Engineering

2024

Vol. 50, nr 3

89--197

Organophosphate pesticide pollution is a severe global environmental issue, posing significant threats to human health. This study focuses on an organophosphate pesticide production site in North China. Controlled soil and groundwater were sampled using grid sampling and direct-push technology. Analytical methods such as headspace gas chromatography-mass spectrometry (GC-MS), gas chromatography (GC), and purge and trap GC-MS were employed to detect contaminants, including volatile organic compounds (VOCs), semi-volatile organic compounds (SVOCs), pesticides, and heavy metals. The results indicate that the maximum exceedances in the contaminated soil for benzene, xylene, tri-chloromethane, Parathion, phosphorodithioic acid, Phorate, and Terbufos were 490.63, 411.22, 3459.09, 507.89, 277.29, 1946.15, and 281.5 times the standard limits, respectively. In groundwater, benzene, dichloromethane, trichloromethane, and Phorate exceeded the standard limits by 220.83, 1374, 1853.33, and 806.67 times, respectively, severely contaminating the local soil and groundwater and damaging the eco-system. Additionally, the carcinogenic risk values in the contaminated soil for benzene, toluene, phenylethane, xylene, 1,3,5-trimethylbenzene, 1,2,4-trimethylbenzene, Parathion, 1,2,4-trichlorobenzene, tri-chloromethane, naphthalene, and aniline were 2.75E–3, 1.36E–2, 1.034E–2, 9.72E–1, 1.5E–3, 8.9E–3, 1.85E–3, 4E–3, 3.48E–2, 2.7E–2, 1.03E–3, respectively. In groundwater, toluene, xylene, dichloromethane, and trichloromethane showed carcinogenic risk values of 8E–3, 1.86E–2, 1.23E–2, 3.07E–2, respectively, significantly exceeding the acceptable limits and posing severe threats to the health of nearby residents. Furthermore, the hazard quotient for non-carcinogenic effects of Cypermethrin in soil was 1.15, and 2,6-dichlorophenol in groundwater was 1.22, both slightly above the standard, indicating a mild impact on the health of nearby residents. Overall, this study provides a theoretical basis for subsequent remediation work at the contaminated site through pollutant analysis.

A single upper limb pose estimation method based on the improved stacked hourglass network

Peng Gang, Zheng Yuezhi, Li Jianfeng, Yang Jin

International Journal of Applied Mathematics and Computer Science

2021

Vol. 31, no. 1

123--133

At present, most high-accuracy single-person pose estimation methods have high computational complexity and insufficient real-time performance due to the complex structure of the network model. However, a single-person pose estimation method with high real-time performance also needs to improve its accuracy due to the simple structure of the network model. It is currently difficult to achieve both high accuracy and real-time performance in single-person pose estimation. For use in human–machine cooperative operations, this paper proposes a single-person upper limb pose estimation method based on an end-to-end approach for accurate and real-time limb pose estimation. Using the stacked hourglass network model, a single-person upper limb skeleton key point detection model is designed. A deconvolution layer is employed to replace the up-sampling operation of the hourglass module in the original model, solving the problem of rough feature maps. Integral regression is used to calculate the position coordinates of key points of the skeleton, reducing quantization errors and calculations. Experiments show that the developed single-person upper limb skeleton key point detection model achieves high accuracy and that the pose estimation method based on the end-to-end approach provides high accuracy and real-time performance.