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The purpose of this paper is to empirically investigate the impact of economic policy uncertainty on firms' labor investment decision, which includes labor investment level and efficiency, especially human capital allocation.

This paper uses Economic Policy Uncertainty Index for China and Chinese A-share listed firms in the period 2002–2016 to constructs a sample of 20,779 firm-year observations and applies the methods of pooled OLS regressions to do an empirical study.

This paper finds that firms' labor investment is negatively correlated with economic policy uncertainty. And firms' labor investment efficiency (and overinvestment in labor) is positively (negatively) correlated with economic policy uncertainty, which is more significant for non-SOEs and firms with less government intervention. Further, the positive relation between economic policy uncertainty and labor investment efficiency is more significant for labor-intensive firms, firms in competitive industry, firms in developed labor market and firms under strong labor law protection. In addition, economic policy uncertainty induces firms to make adjustment on human capital structure and allocate more employees with high human capital, which eventually helps firms achieve higher total factor productivity.

The study of this paper indicates that the government needs to consider economic policies' impact on firms when introducing and changing policies and guide firms to improve human capital allocation under different internal and external conditions to finally realize the optimal allocation of social resources.

This paper studies the influence of external economic policy environment on firms' labor investment decision, which lacks adequate attention in the literature and indicates that under economic policy uncertainty, firms actively decrease labor demand and increase labor investment efficiency by optimizing human capital allocation.

Large capacity current carrier printed circuit board (PCB) imposes strict control requirements on the hole wall roughness. The key factors are chip removal, drilling temperature and tool wear. This paper aims to find out a cryogenic drilling process to control the chip removal, chip morphology, tool wear and finally reduce the hole wall roughness.

The chip removal process, chip morphology, tool wear and hole wall roughness of glass fiber epoxy resin copper clad laminate (FR-4) drilling were observed and analyzed. The influence of cold air on the chip removal process, chip morphology, tool wear and hole wall roughness was also investigated. An optimization process of cold air auxiliary drilling was proposed to control the hole wall roughness of FR-4.

The results showed that the discharge time of copper foil chips with obvious characteristics can be used as the evaluation criterion for the smoothness of chip removal. The cold air can promote chip removal and reduce tool wear. In addition, the chip removal and cooling performance will be the best when using −4.7 °C cold air with the injection angle consisted with the angle of helical flute of the drill. The hole wall roughness of FR-4 could be controlled by drilling with −4.7°C cold air.

This paper was the first study of the effect of three kinds of cold air on PCB drilling. This provided a reference for the possibility that the cryogenic drilling methods apply to PCB drilling. A new cold air auxiliary drilling process was developed for large capacity current carrier FR-4 manufacturing.

In shield tunneling projects, human, shield machine and underground environment are tightly coupled and interacted. Accidents often occur under dysfunctional interactions among them. Therefore, this paper aims to develop a multi-agent based safety computational experiment system (SCES) and use it to identify the main influential factors of various aspects of human, shield machine and underground environment.

The methods mainly comprised computational experiments and multi-agent technologies. First, a safety model with human-machine-environment interaction consideration is developed through the multi-agent technologies. On this basis, SCES is implemented. Then computational experiments are designed and performed on SCES for analyzing safety performance and identifying the main influential factors.

The main influential factors of two common accidents are identified. For surface settlement, the main influential factors are ranked as experience, soil density, soil cohesion, screw conveyor speed and thrust force in descending order of influence levels; for mud cake on cutter, they are ranked as soil cohesion, experience, cutter speed and screw conveyor speed. These results are consistent with intuition and previous studies and demonstrate the applicability of SCES.

The proposed SCES provides comprehensive risk factor identification for shield tunneling projects and also insights to support informed decisions for safety management.

A safety model with human-machine-environment interaction consideration is developed and computational experiments are used to analyze the safety performance. The novel method and model could contribute to system-based safety research and promote systematic understanding of the safety performance of shield tunneling projects.