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With the growing concern of various sectors of society regarding environmental issues and the promotion of sustainable development, green technology innovation is generally considered to be conducive to the long-term development of enterprises. However, because of the existence of agency problems, managers may have shortsighted behaviors. Then how will managers' shortsighted behaviors affect enterprises' green technology innovation?

This paper uses machine learning-based text analysis methods to construct a manager myopia index based on the data from A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2015 to 2020. We examine the impact of manager myopia on green technology innovation in companies.

Our study finds that manager myopia significantly inhibits green technology innovation in companies. However, when multiple large shareholders coexist and the proportion of institutional investors' holdings is high, it can alleviate the inhibitory effect of manager myopia on green innovation. Heterogeneity tests show that the impact of manager myopia on green technology innovation is relatively significant in non-state-owned and manufacturing companies, as well as in the electricity industry. Robustness tests demonstrate that our conclusions remain valid after using propensity score matching to eliminate endogeneity problems.

From the perspective of corporate governance, this paper incorporates managers' shortsightedness, multiple large shareholders and institutional investors' shareholding ratios into the same logical framework, analyzes their internal mechanisms, helps improve corporate governance, enhances green innovation capabilities and has strong implications for the implementation of national innovation-driven development strategies and the achievement of “carbon peak” and “carbon neutrality” targets.

In cloth animation field, the collision detection of fabric under external force is very complex, and difficult to satisfy the needs of reality feeling and real time. The purpose of this paper is to improve reality feeling and real-time requirement.

This paper puts forward a mass-spring model with building bounding-box in the center of particle, and designs the collision detection algorithm based on Mapreduce. At the same time, a method is proposed to detect collision based on geometric unit.

The method can quickly detect the intersection of particle and triangle, and then deal with collision response according to the physical characteristics of fabric. Experiment shows that the algorithm improves real-time and authenticity.

Experiments show that 3D fabric simulation can be more efficiency through parallel calculation model − Mapreduce.

This method can improve the reality feeling, and reduce calculation quantity.

This collision-detection can be used into more fields such as 3D games, aero simulation training and garments automation.

This model and method have originality, and can be used to 3D animation, digital entertainment, and garment industry.