Qunfeng Zeng, Hao Jiang, Qi Liu, Gaokai Li and Zekun Ning
This paper aims to introduce a high-temperature grease design method assisted by back propagation neural network (BPNN) and verify its application value.
Abstract
Purpose
This paper aims to introduce a high-temperature grease design method assisted by back propagation neural network (BPNN) and verify its application value.
Design/methodology/approach
First, the grease data sets were built by sorting out the base data of greases in a large number of literatures and textbooks. Second, the BPNN model was built, trained and tested. Then, the optimized BPNN model was used to search the unknown data space and find the composition of greases with excellent high-temperature performance. Finally, a grease was prepared according to the selected composition predicted by the model and the high-temperature physicochemical performance, high-temperature stability and tribological properties under different friction conditions were investigated.
Findings
Through high temperature tribology experiments, thermal gravimetric analysis and differential scanning calorimetry experiments, it is proved that the high temperature grease prepared based on BPNN has good high-temperature performance.
Originality/value
To the best of the authors’ knowledge, a new method of designing and exploring high-temperature greases is successfully proposed, which is useful and important for the industrial applications.
Details
Keywords
Wenxing Liu, Kong Zhou, Xi Ouyang, Siyuan Chen and Kai Gao
In recent years, organizations have progressively adopted electronic performance monitoring (EPM) to obtain accurate employee performance data and improve management efficiency in…
Abstract
Purpose
In recent years, organizations have progressively adopted electronic performance monitoring (EPM) to obtain accurate employee performance data and improve management efficiency in response to the growing complexity of the work environment. However, existing research has primarily focused on examining the effect of EPM on employee behaviors within established job designs, neglecting the consequential role of EPM in shaping employees’ bottom-up job redesign (i.e. job crafting). This study aims to explore whether and how EPM affects employee job crafting.
Design/methodology/approach
To test proposed hypotheses, we conducted two time-lagged surveys across different cultural contexts and a scenario experiment on an online platform in China.
Findings
The results revealed the negative indirect relationship between EPM and employee job crafting via role breadth self-efficacy. This indirect relationship was moderated by constructive supervisor feedback and job complexity, with the above relationships being weak (versus strong) when constructive supervisor feedback was high (versus low) or job complexity was low (versus high).
Practical implications
The results have crucial implications for organizational practices, suggesting that managers should provide constructive feedback to break the trade-off between EPM and job crafting. Additionally, managers may need to give employees with high job complexity more autonomy rather than intense monitoring.
Originality/value
This study is the first to clarify the effect of EPM on employee job crafting. As job crafting captures the important value of employees in organizational job design, our effort helps to enrich the understanding of EPM effectiveness.