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With the rapid low-carbon transformation in China, the industrial approach and labor structure of mining enterprises are undergoing constant changes, leading to an increasing psychological dilemma faced by coal miners. This study aims to reveal the relationship and mechanism of factors influencing the psychological dilemma of miners, and to provide optimal intervention strategies for the safety and sustainable development of employees and enterprises.

To effectively address the complex issue of the psychological dilemma faced by miners, this study identifies and constructs five-dimensional elements, comprising 20 indicators, that influence psychological dilemmas. The relational mechanism of action of factors influencing psychological dilemma was then elucidated using an integration of interpretive structural modeling and cross-impact matrix multiplication.

Industry dilemma perception is a “direct” factor with dependent attributes. The perceptions of management response and relationship dilemmas are “root” factors with driving attributes. Change adaptation dilemma perception is a “susceptibility” factor with linkage attributes. Work dilemma perception is a “blunt” factor with both dependent and autonomous attributes.

The aforementioned findings offer a critical theoretical and practical foundation for developing systematic and cascading intervention strategies to address the psychological dilemma mining enterprises face, which contributes to advancing a high-quality coal industry and efficient energy development.

Robotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.

This study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.

Experiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.

A new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

This study aims to explore the impact of supply chain quality event (SCQE) announcements on enterprises’ stock market value.

This study adopts the event study approach and analyzes the changes in shareholder value of companies listed in China based on data from 118 SCQE announcements. In the event study, the market, market-adjusted and Carhart four-factor models are used to estimate abnormal stock market returns, and a cross-sectional regression model is performed to examine the effects of SCQE announcements on enterprises’ stock market value.

SCQE announcements have a negative impact on shareholder value. From the perspective of the supply chain network structure, the market reacts more negatively to SCQE announcements issued by the enterprises with higher supply chain concentration. From the perspective of companies’ characteristics, announcements that do not reflect the establishment of supply chain quality cooperation have a more negative effect on stock market value, which indicates that the supply chain network structure and firm-level characteristic can moderate the market reaction.

The findings demonstrate a quantitative evaluation of how SCQE announcements affect the stock market value of listed companies and provide guidance for managers to enhance the value of SCQE announcements.

This study fills the research gap on the impact of SCQE announcements on stock market value by using secondary data and first explores the relationship between SCQE announcements and stock market value from the perspective of supply chain network. Furthermore, this study contributes to the literature on SCQE using an empirical study in China.