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The purpose of this study is to examine the impact of cross-ownership on corporate digital innovation and their specific mechanisms. Cross-ownership, who hold equity in two or more companies simultaneously, have two different types of governance effects in the capital market: governance synergistic effects and competitive collusion effects.

This paper uses a panel model, selecting A-share company data from 2011 to 2021 in China. In total, 23,853 valid data were obtained, which came from the CSMAR database and Wind database. For some missing data, they were manually supplemented by consulting the company's annual report and Sina Finance. Data processing was conducted using EXCEL and Stata16.0 software.

The results show that cross-ownership promote corporate digital innovation by leveraging governance synergies. Further grouping tests show that the synergistic effects of cross-ownership are significant in non-state-owned, high-tech, weakly competitive and higher analyst attention enterprises. Mechanism testing shows that cross-ownership can empower corporate digital innovation in three ways: reducing information asymmetry, alleviating financing constraints and improving corporate governance.

The conclusion of this paper provides new empirical evidence for a comprehensive understanding of the role of cross-ownership in corporate development, enriches the economic consequences research of chain institutional investors in China and broadens the research perspective of corporate digital innovation. It also provides important references for the digital transformation of enterprises and the healthy development of the capital market.

Porosity is a commonly analyzed defect in the laser-based additive manufacturing processes owing to the enormous thermal gradient caused by repeated melting and solidification. Currently, the porosity estimation is limited to powder bed fusion. The porosity estimation needs to be explored in the laser melting deposition (LMD) process, particularly analytical models that provide cost- and time-effective solutions compared to finite element analysis. For this purpose, this study aims to formulate two mathematical models for deposited layer dimensions and corresponding porosity in the LMD process.

In this study, analytical models have been proposed. Initially, deposited layer dimensions, including layer height, width and depth, were calculated based on the operating parameters. These outputs were introduced in the second model to estimate the part porosity. The models were validated with experimental data for Ti6Al4V depositions on Ti6Al4V substrate. A calibration curve (CC) was also developed for Ti6Al4V material and characterized using X-ray computed tomography. The models were also validated with the experimental results adopted from literature. The validated models were linked with the deep neural network (DNN) for its training and testing using a total of 6,703 computations with 1,500 iterations. Here, laser power, laser scanning speed and powder feeding rate were selected inputs, whereas porosity was set as an output.

The computations indicate that owing to the simultaneous inclusion of powder particulates, the powder elements use a substantial percentage of the laser beam energy for their melting, resulting in laser beam energy attenuation and reducing thermal value at the substrate. The primary operating parameters are directly correlated with the number of layers and total height in CC. Through X-ray computed tomography analyses, the number of layers showed a straightforward correlation with mean sphericity, while a converse relation was identified with the number, mean volume and mean diameter of pores. DNN and analytical models showed 2%–3% and 7%–9% mean absolute deviations, respectively, compared to the experimental results.

This research provides a unique solution for LMD porosity estimation by linking the developed analytical computational models with artificial neural networking. The presented framework predicts the porosity in the LMD-ed parts efficiently.