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This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork.

This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period.

Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers.

The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

The transition to green growth goals requires the concerted efforts of the whole society. Enterprises, as important players in the market, play a key role in promoting green and sustainable development. The rise of the concept of sustainable development has enabled more enterprises to disclose environmental, social and governance (ESG) information, and ESG behaviour is regarded as a positive strategic behaviour to implement the new development concept. This paper aims to explore the influence of ESG performance on enterprise green innovation.

This study applies a fixed effect model and the regulation effect of empirical analysis to explore the influence of ESG performance on enterprise green innovation. The object of investigation is 2014–2021 Shanghai and Shenzhen A-share listed companies.

The results of an empirical analysis outline the following conclusions: (1) ESG performance has a significant effect on enterprise green innovation, mainly by easing the pressure of the financing enterprise, fitting stakeholders’ environmental protection concept and obtaining employee organizational identity that influences enterprise green innovation. (2) Government regulation positively regulates the role of ESG performance in promoting the green innovation of enterprises. (3) Heterogeneity analysis found that the strengthening role of ESG performance on the green innovation of enterprises is stronger in green invention patents, state-owned enterprises and nonheavily polluting industries.

Despite the valuable findings, this study has a few limitations. Thus, it is necessary to extend the object of investigation by adding other Asian countries, which allows for comparison analysis and allocating best practices for promoting green innovation. Besides, innovation and ESG performance depend on the quality of institutions. In this case, the future study should incorporate the indicators that reveal the quality of institutions (corruption, transparency, digitalisation, voice, accountability, etc.).

According to the above conclusions, this paper proposes suggestions at the level of enterprises, government and investors. At the enterprise level, ESG responsibility should be strengthened, ESG information should be consciously disclosed and the quality of ESG disclosure should be improved. Government departments should play the role of supervisors, improve the construction of ESG information disclosure systems and promote the formation of ESG systems. At the social level, investors should improve the ESG information status and pay more attention to the ESG performance of enterprises.

This study fills the scientific gaps in the analysis impact of ESG performance on the green innovation of enterprises. This paper contributes to the theoretical landscape of ESG efficiency by developing approaches based on two empirical models: testing the impact of enterprise ESG performance on green innovation and testing whether government regulation plays a regulatory role in the relationship between ESG performance and green innovation. Besides, this study analysed the ESG performance and green innovation within the following categories: heavy and nonheavy polluter industries; state and nonstate-owned enterprise groups.

The purpose of this study is to explore how to select a suitable supply chain collaboration paradigm (SCCP) based on the intelligent manufacturing model (IMM) of enterprises.

Given the fit between internal collaboration and external collaboration, we propose a model to select a suitable SCCP based on two-sided matching between SCCPs and IMMs. In this decision problem, we invited five university scholars and seven related consultants to evaluate SCCPs and IMMs based on the regret theory, which is used to obtain the perceived utility and matching results. The evaluation values are comfortably expressed through probabilistic linguistic term sets (PLTSs). Also, we set the lowest acceptance threshold to improve the accuracy of matching results.

The findings indicate that the characteristics of IMMs can significantly influence the selection of SCCPs, and an SCCP is not suitable for all IMMs. Interestingly, the study findings suggest that the selection of SCCP is diverse and multi-optional under the constraints of IMMs.

Existing studies have explored supply chain collaboration (SCC) in Industry 4.0 to improve supply chain performance, but less attention has been paid to the impact of the match between SCCPs and IMMs on supply chain performance. And even fewer studies have addressed how to select a suitable SCCP in different IMMs. This study provides a unique contribution to the practice of SCC and expands the understanding of supply chain management in Industry 4.0.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

The purpose of this paper is to combine triboelectric nanogeneration technology with ball bearing structure to achieve energy collection and fault monitoring.

In this paper, according to the rotation mode of ball bearings, the freestanding mode of triboelectric nanogeneration is selected to design and manufacture a novel triboelectric nanogeneration device Rolling Ball Triboelectric Nanogenerator (RB-TENG) which combines rotary energy collection with ball bearing fault self-sensing.

The 10,000s continuous operation experiment of the RB-TENG is carried out to verify its robustness. The accurate feedback relationship between the RB-TENG and rotation velocity can be demonstrated by the fitting comparison between the theoretical and experimental electrical signal periods at a certain time. By comparing the output electrical signals of the normal RB-TENG and the rotor spalling RB-TENG and polytetrafluoroethylene (PTFE) balls with different degrees of wear at 500 r/min, it can be concluded that the RB-TENG has an ideal monitoring effect on the radial clearance distance of bearings. The spalling fault test of the RB-TENG stator inner ring and rotor outer ring is carried out.

Through coupling experiments of rotor spalling fault of the RB-TENG and PTFE balls fault with different degrees of wear, it can be seen that when rotor spalling fault occurs, balls wear has a greater impact on the normal operation of the RB-TENG, and it is easier to identify. The fault self-sensing ability of the RB-TENG can be obtained, which is expected to provide an effective scheme for monitoring the radial wear clearance distance of ball bearings.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0295/