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This paper aims to explore the impact of product market competition (PMC) on companies’ investment in external auditing.

This paper applies a conceptual framework derived from demand–supply analysis and a panel data set of 2,263 listed manufacturing companies in China covering the period 2012–2019. In the assessment of PMC, this study measures industry-level competition intensity and company-specific market power separately.

Industries appear to engage in a lower average level of external auditing if industrial competition intensity is either too high or too low. Similarly, companies spend less on external auditing if their market power is either too strong or too weak, and the company-level inverted U-shaped relationship is much more evident in industries with weak PMC.

This paper shows that a company’s external audit strategy is affected by the level of competition it faces in its market. The findings of this paper can improve the current linear PMC–auditing theoretical framework and provide insights into the strategic auditing of listed companies in China. The findings also have significant implications for policy recommendations regarding corporate governance and market scrutiny regulations.

This paper aims to develop a demand scale from the perspective of scientific data providers and to analyze their demands, offering references for research and practice in scientific data sharing.

The initial scale was designed based on a literature review. A total of 479 valid responses from data providers were collected via questionnaires. Exploratory and confirmatory factor analyses were conducted using SPSS21.0 and AMOS23.0, followed by a discussion on practical implications.

Providers exhibit significant demands in all dimensions, with data security being the most urgent, followed by data management platforms and self-value realization. Additionally, the prioritization of providers’ demands varies according to their intentions to share.

The study developed a scale of providers’ demands in scientific data sharing that comprises 21 items across five dimensions: data security, data management platform, self-value realization, social and benefits return and analyzed the demand degree of providers for the above items. Finally, the paper proposes strategies from stakeholders’ perspectives to meet providers’ demands and facilitate scientific data sharing.

In recent years, to solve the contradiction between energy supply and demand, the Chinese Government has vigorously promoted shale gas development. With the rapid development of the shale gas industry, the environmental impact problems have become increasingly serious. Therefore, it is of great significance to carry out a comprehensive environmental impact assessment of shale gas development. This study aims to provide a theoretical basis for enterprises to make development decisions on shale gas projects by constructing a model of comprehensive environmental impact assessment for shale gas development.

In this paper, the comprehensive environmental impact factors of shale gas development are analyzed from the two aspects of the natural environment and macro environment, and the index system of comprehensive environmental impact assessment for shale gas development including 7 secondary indicators and 24 tertiary indicators is constructed. Owing to the fact that qualitative indicators are difficult to quantify in the evaluation process, the method of intuitionistic fuzzy analytic hierarchy process (IFAHP) is adopted for evaluation. This method (IFAHP) can delicately describe the hesitancy degree of the decision-makers in the process of assigning a weight to the indicators, and make the weight assignment of each index more accurate. Furthermore, this method overcomes the shortcomings of the conventional methods, such as the complexity of calculation and the large amount of calculation.

The evaluation model is applied to a shale gas platform drilling project in Southwest China. Based on the ratings from 13 experts, the comprehensive environmental impact assessment grade of this project is good, indicating that the shale gas development project is feasible. The result is basically in line with the actual situation.

Based on the consideration of the natural environmental impacts of shale gas development, this paper also has considered the macro environmental impact of shale gas development, and has established the index system of comprehensive environmental impact assessment for shale gas development from the two aspects of the natural environment and macro environment. To overcome such difficulties as incomplete evaluation by decision-makers, cumbersome calculation process and a large amount of calculation, this paper has adopted the method of IFAHP to evaluate and has established a comprehensive environmental impact assessment model for shale gas development based on IFAHP.

The purpose of this study is to detail the design and development of a robust and practical perception system for autonomous material handling robots (AMHRs) operating within industrial stockyards. This system aims to support simultaneous localization and mapping (SLAM) while generating large-scale spatial cognition, ensuring accurate, low-latency, and scalable operations in demanding industrial environments.

The proposed perception system integrates multimodal perception sensors, efficient algorithms and commercial hardware devices to o provide SLAM-based large-scale spatial cognition for distributed AMHRs. The system’s design emphasizes practicality, efficiency and readiness for real-world deployment, ensuring it meets the stringent requirements of accuracy, latency and scalability.

Experiments conducted in a real industrial stockyard environment demonstrate the practicality and robustness of the perception system. The system exhibits high performance in state estimation, stockpile modeling accuracy and motion spatial cognition, confirming its effectiveness for AMHR operations.

The developed system was practically used at Tianjin Port, demonstrating the potential for widespread industrial application, offering a scalable and efficient solution for AMHR operations. Its integration into diverse industrial settings can lead to significant improvements in material handling processes, contributing to enhanced productivity and operational efficiency.

This work presents an innovative perception system that combines advanced SLAM-based spatial cognition akin to that of the brain with practical deployment considerations. The system’s design and implementation address the specific challenges of AMHRs in industrial environments, providing a novel solution for enhancing the operational efficiency and adaptability of autonomous robots in stockyards and similar settings.