ObjectivesCoal workers’ pneumoconiosis is caused by exposure to respirable coal mine dust. The self-protection of underground coal miners (UCM) plays an irreplaceable role against this threat. The purpose of this study was to evaluate the predictors of self-protective behaviour (SPB) in Chinese UCM based on the health belief model (HBM).MethodsA cross-sectional study was conducted among 341 UCM in January 2016 in Pingdingshan City, Henan Province, People’s Republic of China. Data was collected using a self-reported questionnaire, which included questions on sociodemographic characteristics, HBM variables and SPB. An exploratory factor analysis of the principal components with varimax rotation was carried out on the HBM-related items. Pearson’s correlation and multiple linear regression analysis were used to assess the relationship between sociodemographic characteristics, HBM variables and SPB.ResultsMultiple regression analysis demonstrated the monthly income (B=0.403, p=0.001), the internal cues to action (B=0.380, p<0.001) and external cues to action (B=0.401, p<0.001) as the predictors of UCM’s SPB, accounting for 24.8% of total variance (F=34.96, p<0.001), while the cognition variables of HBM were not significantly associated with SPB.ConclusionThe results suggested that both internal and external cues to action were powerful predictors for SPB. These findings highlight that further efforts are required to provide the UCM with periodic health check-up reports and promote the active role of doctors and family members in miners’ decision-making to simulate them for better SPB.Ethics approvalAll data collection procedures received ethical approval from the Ethics Committee of Xuhui District Centre for Disease Control and Prevention (XHHEC-2016–7).

Purpose The purpose of this paper is to enhance control accuracy, energy efficiency and productivity of customized industrial robots by the proposed multi-objective trajectory optimization approach. To obtain accurate dynamic matching torques of the robot joints with optimal motion, an improved dynamic model built by a novel parameter identification method has been proposed. Design/methodology/approach This paper proposes a novel multi-objective optimal approach to minimize the time and energy consumption of robot trajectory. First, the authors develop a reliable dynamic parameters identification method to obtain joint torques for formulating the normalized energy optimization function and dynamic constraints. Then, optimal trajectory variables are solved by converting the objective function into relaxation constraints based on second-order cone programming and Runge–Kutta discrete method to reduce the solving complexity. Findings Extensive experiments via simulation and in real customized robots are conducted. The results of this paper illustrate that the accuracy of joint torque predicted by the proposed model increases by 28.79% to 79.05% over the simplified models used in existing optimization studies. Meanwhile, under the same solving efficiency, the proposed optimization trajectory consumes a shorter time and less energy compared with the existing optimization ones and the polynomial trajectory. Originality/value A novel time-energy consumption optimal trajectory planning method based on dynamic identification is proposed. Most existing optimization methods neglect the effect of dynamic model reliability on energy efficiency optimization. A novel parameter identification approach and a complete dynamic torque model are proposed. Experimental results of dynamic matching torques verify that the control accuracy of optimal robot motion can be significantly improved by the proposed model.

No abstract