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Sufficient sample data are the necessary condition to ensure high reliability; however, there are relatively poor fatigue test data in the engineering, which affects fatigue life's prediction accuracy. Based on this, this research intends to analyze the fatigue data with small sample characteristics, and then realize the life assessment under different stress levels.

Firstly, the Bootstrap method and the principle of fatigue life percentile consistency are used to realize sample aggregation and information fusion. Secondly, the classical outlier detection algorithm (DBSCAN) is used to check the sample data. Then, based on the stress field intensity method, the influence of the non-uniform stress field near the notch root on the fatigue life is analyzed, and the calculation methods of the fatigue damage zone radius and the weighting function are revised. Finally, combined with Weibull distribution, a framework for assessing multiaxial low-cycle fatigue life has been developed.

The experimental data of Q355(D) material verified the model and compared it with the Yao’s stress field intensity method. The results show that the predictions of the model put forward in this research are all located within the double dispersion zone, with better prediction accuracies than the Yao’s stress field intensity method.

Aiming at the fatigue test data with small sample characteristics, this research has presented a new method of notch fatigue analysis based on the stress field intensity method, which is combined with the Weibull distribution to construct a low-cycle fatigue life analysis framework, to promote the development of multiaxial fatigue from experimental studies to practical engineering applications.

Explore the impact of Party organization embedding on firm green governance.

The regression analysis method.

The findings show that Party organization embedding significantly enhances the green governance effects of firms, with this effect being more pronounced in environments with high-quality internal control. Moreover, the study reveals that Party organization embedding facilitates green governance through mechanisms such as reducing agency costs and optimizing management decisions. Agency costs have a negative transmission effect, while management decisions have a positive transmission effect, with the quality of internal control playing a crucial moderating role.

Most existing studies on firm green governance have focused on aspects such as the heterogeneity of management teams (Liu, 2019; Wu et al., 2019), executive green cognition (Fineman and Clarke, 1996; Huang and Wei, 2023), organizational structure and the involvement of controlling families (Bertrand and Schoar, 2006; Symeou et al., 2019), with limited attention to the unique role of Party organizations’ incentive and restraint mechanisms, supervisory power and management functions in firm green governance. Additionally, while scholars have examined the impact of political embedding in firms, including Party organization embedding as a specific form of political embedding, and find that it affects various aspects of business performance (Chang and Wong, 2004; Gu and Yang, 2023), governance quality (Li et al., 2020; Huang and Yang, 2024), agency costs (Qian, 2000; Wang and Ma, 2014), excessive management compensation (Chang and Wong, 2004; Chen et al., 2014), social externalities and audit needs (Faccio, 2006; Cheng, 2022), there is still insufficient discussion on how Party organization embedding promotes firm green governance. Particularly in the context of China’s unique system and using Chinese data, there is a need for more in-depth research on the impact of Party organization embedding on firm green governance. This paper addresses this research gap by empirical analysis.

Overall, this study has significant theoretical and practical implications. Theoretically, it enriches the literature on Party organization embedding and firm green governance, filling a gap in the intersection research of firm governance and green governance. Practically, on the one hand, this paper’s findings demonstrate that the involvement of Party organizations in firm governance plays a significant role in enhancing green governance. This supports the modernization of firm governance in China, establishes a micro-level foundation for achieving the strategic goals of “carbon peaking and carbon neutrality” and offers empirically-backed insights into green transformation for policymakers. The research also provides practical policy recommendations for strengthening Party building efforts within firms and optimizing government-business relations, thereby facilitating the deep integration of Party building with business operations. On the other hand, this study highlights that the unique feature of China’s corporate governance system, Party organization embedding, can effectively enhance green governance. This offers empirical support for leveraging the strengths of China’s firm governance model and provides valuable governance strategies for firms in other countries and regions to improve their green governance practices.

This study’s social implications are significant as it highlights the broader societal benefits that arise from integrating Party organization involvement into firm governance structures, especially within the context of green governance. By improving the green governance practices of firms, Party organization embedding helps to address pressing environmental issues such as pollution, carbon emissions and resource depletion, which ultimately contributes to healthier living environments and a more sustainable society. The emphasis on green governance supports China’s national strategy for sustainable development and demonstrates a governance model that balances economic growth with environmental stewardship. Additionally, the study underscores the role of Party organizations in fostering social responsibility, equity and cohesion by ensuring that firm decision-making aligns with both economic and social welfare goals. This model of governance provides a framework that can serve as a reference for other countries and regions looking to enhance environmental protection efforts while maintaining social stability and economic progress.

This study offers original insights by exploring the distinctive role of Party organization embedding in enhancing firm green governance within the unique context of China’s political and economic systems. Unlike previous research, which has primarily focused on conventional governance structures, this paper delves into the underexplored area of how Party organizations influence firm-level green governance. By examining the direct and indirect effects of Party organization embedding, this study expands current understanding of corporate governance models that integrate political structures, providing a novel perspective on how firms can achieve both economic and environmental objectives. The findings not only contribute to the literature on green governance but also present a valuable model for emerging economies that are pursuing sustainable development. This research thus provides a meaningful addition to the dialogue on corporate governance innovation and environmental responsibility.

This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.

The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.

Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.

This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.