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This paper takes the Shanghai-Shenzhen-Hong Kong Stock Connect as a quasi-natural experiment and investigates the impact of capital market liberalization on the corporate debt maturity structure. It also aims to provide some policy implications for corporate debt financing and further liberalization of the capital market in China.

Employing the exogenous event of Shanghai-Shenzhen-Hong Kong Stock Connect and using the data of Chinese A-share firms from 2010 to 2020, this study constructs a difference-in-differences model to examine the relationship between capital market liberalization and corporate debt maturity structure. To validate the results, this study performed several robustness tests, including the parallel test, the placebo test, the Heckman two-stage regression and the propensity score matching.

This paper finds that capital market liberalization has significantly increased the proportion of long-term debt of target firms. Further analyses suggest that the impact of capital market liberalization on the debt maturity structure is more pronounced for firms with lower management ownership and non-Big 4 audit. Channel tests show that capital market liberalization improves firms’ information environment and curbs self-interested management behavior.

This research provides empirical evidence for the consequences of capital market liberalization and enriches the literature on the determinants of corporate debt maturity structure. Further this study makes a reference for regulators and financial institutions to improve corporate financing through the governance role of capital market liberalization.

By incorporating the defect feature information, an ML-based linkage between defects and fatigue life unaffected by the time scale is developed, the primary focus is to quantitatively assess and elucidate the impact of different defect features on fatigue life.

A machine learning (ML) framework is proposed to predict the fatigue life of LPBF-built Hastelloy X utilizing microstructural defects identified through nondestructive detection prior to fatigue testing. The proposed method combines nondestructive micro-computerized tomography (micro-CT) technique to comprehensively analyze the size, location, morphology and distribution of the defects.

In the test set, SVM-based fatigue life prediction exhibits the highest accuracy. Regarding the defect information, the defect size significantly affects fatigue life, and the diameter of the circumscribed sphere of the largest defect has a critical effect on fatigue life.

This comprehensive approach provides valuable insights into the fatigue mechanism of structural materials in defective states, offering a novel perspective for better understanding the influence of defects on fatigue performance.

To improve the accuracy of parameter prediction for small-sample data, considering the existence of error in samples, the error circle is introduced to analyze original samples.

The influence of surface roughness on fatigue life is discussed. The error circle can treat the original samples and extend the single sample, which reduces the influence of the sample error.

The S-N curve obtained by the error circle method is more reliable; the S-N curve of the Bootstrap method is more reliable than that of the Maximum Likelihood Estimation (MLE) method.

The parameter distribution and characteristics are statistically obtained based on the surface roughness, surface roughness factor and intercept constant. The original sample is studied by an error circle and discussed using the Bootstrap and MLE methods to obtain corresponding S-N curves. It provides a more trustworthy basis for predicting the useful life of products.