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This paper aims to address the challenges of the capacitor tower maintenance robot during bolt tightening in high-voltage substations, including difficulties in bolt positioning due to tilted angles and anti-bird cover occlusion and issues with fast and accurate docking of bolts while the base is moving.

This paper proposes a visual servoing method for the capacitor tower maintenance robot, including bolt pose estimation and visual servoing control. Bolt pose estimation includes four components: constructing a keypoint detection network to identify the approximate position, precise positioning, rapid prediction and calculation of bolt pose. In visual servoing, an improved position-based visual servoing (PBVS) is proposed, which eliminate steady-state error and enhance response speed during dynamic tracking by incorporating integral and differential components.

The bolt detection method exhibits high robustness against varying lighting conditions, partial occlusions, shooting distances and angles. The maximum positioning error at a distance of 250 mm is 2.8 mm. The convergence speed of the improved PBVS is 10% higher than that of the traditional PBVS when the base and target remain relatively stationary. When the base moves at a constant speed, the improved method eliminates steady-state error in dynamic tracking. When the base moves rapidly and intermittently, the maximum error of the improved method in the tracking process is 30% smaller than that of traditional PBVS.

This method enables real-time detection and positioning of bolts in an unstructured environment with tilt angles, variable lighting conditions and occlusion by anti-bird covers. An improved PBVS is proposed to enhance its capability in tracking dynamic targets.

Library intelligence institutions, which are a kind of traditional knowledge management organization, are at the frontline of the big data revolution, in which the use of unstructured data has become a modern knowledge management resource. The paper aims to discuss this issue.

This research combined theme logic structure (TLS), artificial neural network (ANN), and ensemble empirical mode decomposition (EEMD) to transform unstructured data into a signal-wave to examine the research characteristics.

Research characteristics have a vital effect on knowledge management activities and management behavior through concentration and relaxation, and ultimately form a quasi-periodic evolution. Knowledge management should actively control the evolution of the research characteristics because the natural development of six to nine years was found to be difficult to plot.

Periodic evaluation using TLS-ANN-EEMD gives insights into journal evolution and allows journal managers and contributors to follow the intrinsic mode functions and predict the journal research characteristics tendencies.

Soft grippers have safer and more adaptable human–machine and environment–machine interactions than rigid grippers. However, most soft grippers with single gripping postures have a limited gripping range. Therefore, this paper aims to design a soft gripper with variable gripping posture to enhance the gripping adaptability.

This paper proposes a novel soft gripper consisting of a conversion mechanism and four spring-reinforced soft pneumatic actuators (SSPAs) as soft fingers. By adjusting the conversion mechanism, four gripping postures can be achieved to grip objects of different shapes, sizes and weights. Furthermore, a quasi-static model is established to predict the bending deformation of the finger. Finally, the bending angle of the finger is measured to validate the accuracy of the quasi-static model. The gripping force and gripping adaptability are tested to explore the gripping performance of the gripper.

Through experiments, the results have shown that the quasi-static model can accurately predict the deformation of the finger; the gripper has the most significant gripping force under the parallel posture, and the gripping adaptability of the gripper is highly enhanced by converting the four gripping postures.

By increasing the gripping posture, a novel soft gripper with enhanced gripping adaptability is proposed to enlarge the gripping range of the soft gripper with a single posture. Furthermore, a quasi-static model is established to analyze the deformation of SSPA.

This research aims to present a novel cooperative control architecture designed specifically for roads with variations in height and curvature. The primary objective is to enhance the longitudinal and lateral tracking accuracy of the vehicle.

In addressing the challenges posed by time-varying road information and vehicle dynamics parameters, a combination of model predictive control (MPC) and active disturbance rejection control (ADRC) is employed in this study. A coupled controller based on the authors’ model was developed by utilizing the capabilities of MPC and ADRC. Emphasis is placed on the ramifications of road undulations and changes in curvature concerning control effectiveness. Recognizing these factors as disturbances, measures are taken to offset their influences within the system. Load transfer due to variations in road parameters has been considered and integrated into the design of the authors’ synergistic architecture.

The framework's efficacy is validated through hardware-in-the-loop simulation. Experimental results show that the integrated controller is more robust than conventional MPC and PID controllers. Consequently, the integrated controller improves the vehicle's driving stability and safety.

The proposed coupled control strategy notably enhances vehicle stability and reduces slip concerns. A tailored model is introduced integrating a control strategy based on MPC and ADRC which takes into account vertical and longitudinal force variations and allowing it to effectively cope with complex scenarios and multifaceted constraints problems.

The goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.

The path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.

The algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.

An improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.

The purpose of this paper is to clarify the impact mechanisms and weighting factors of environmental, social and governance (ESG) practices on corporate value through bibliometric analysis and core interpretation of existing literature, further explore whether and under what conditions ESG practices contribute to the corporate value creation, and provide an outlook on future research directions.

Bibliometric method is used to analyze literature co-citation, burst detection and keyword co-occurrence, and literature review method is used to condense important ideas from the existing literature.

Through the review, analysis and summary of the existing literature, this paper finds that the perspectives of risk, information and strategy reflect the key pathways through which ESG practices play a role in avoiding harm and creating value for companies directly or indirectly. Macro, meso and micro factors moderate the direction and extent of the impact. Moreover, considering the relationship between ESG performance and ESG disclosure is key to understanding some contradictory findings.

The search terms limit the articles considered, and therefore, the research framework may be incomplete. Moreover, this article is primarily aimed at the research field and lacks guidance at the practical level.

This paper helps the academic community to deepen its understanding of ESG, moving beyond the question of whether ESG is linked to corporate value to further understand why and under what conditions ESG practices create value for firms.

This paper has great practical significance in motivating companies to actively participate in ESG practices.

The theoretical framework in this paper reveals the black box between enterprise ESG practices and value creation, and clarifies the research boundary of “the relationship between ESG practices and value creation,” contributing to the future research in this field.

Digitalization and supply chain collaboration are central to the successful servitization of manufacturing firms. However, how digitalization interacts with supply chain structure to affect servitization decisions in manufacturing firms has been understudied. In this study, we bridge resource dependence theory (RDT) and information processing theory (IPT) to examine how supply chain concentration interacts with digitalization to affect servitization decisions in manufacturing firms.

We tested the hypotheses using a panel dataset of 1,261 publicly listed machinery manufacturing firms in China. We addressed the endogeneity concerns using the control function approach and conducted multiple tests to ensure the robustness of the results.

We find that both supplier and customer concentration are negatively related to servitization, indicating that concentrated supplier and customer bases are hindrances to manufacturing servitization. Digitalization weakens the negative impact of customer concentration on servitization, but it strengthens the negative impact of supplier concentration on servitization.

The findings extend our understanding of supply chain structure and digitalization as determinants of servitization. This research also offers a nuanced view of how digitalization mitigates the negative impacts of supply chain concentration.

This paper's purpose is to suggest that manufacturers strategically place soft orders for assembly materials with suppliers in Silk Road Economic Belt countries who probably doubt the realization of the soft orders placed.

First, a two-stage Stackelberg competition is constructed, taking into account the supplier's trust level in formulating the decision process in the assembly supply chain. The authors then provide a buyback contract to coordinate the supply chain, in which the manufacturer obtains enough supplies by sharing some of the perceived risks of not fully trusted suppliers. Furthermore, the authors conduct a numerical study to investigate the influence of trust under a decentralized case and a buyback contract.

The authors found that all supply chain partners in Silk Road Economic Belt countries experience potential losses due to not fully trusting certain conditions. The study also shows that, in Silk Road Economic Belt countries, operating under a buyback contract is better than being without one in terms of assembly supply chain performance.

On the one hand, the authors only consider the asymmetry of demand information without considering that of cost structure information. On the other hand, a natural extension of the paper is to integrate single-period transactions into the multi-period transaction problem setting. As all these issues require substantial effort, the authors reserve them for future exploration.

Doing business with not-fully-trustworthy partners in Silk Road Economic Belt countries is risky, and this study reveals how trust works in global cooperation and with strategic reactions in situations of partial trust.

This study aims to explore the impact of carbon neutral announcements on “stock market value” of publicly listed companies in China.

The event study approach is adopted. Market, market-adjusted, Carhart four-factor model and a cross-sectional regression model are employed to examine the impacts of carbon neutral announcements on “stock market value” of Chinese companies based on data from 188 carbon neutral announcements.

Carbon neutral announcements positively impact Chinese shareholder value. Carbon neutral announcements at the strategic level have a more positive and significant impact on Chinese stock market value. Innovative carbon neutral announcements do not significantly cause Chinese stock market reactions. Companies have more positive and significant stock market reactions when the companies make carbon neutral announcements that reflect high supply chain network resilience and heterogeneity and strong supply chain network relationships.

The findings uncover the business value of carbon neutral activities and provide operations managers in developing countries insights into how to improve enterprises' market value by actively implementing carbon neutral activities.

This paper is the first trial to apply an event study to examine the relationship between carbon neutral announcements and Chinese stock market value from the perspective of announcement level and type and supply chain networks. This paper introduces corporate reputation theory and enriches the application of corporate reputation theory in the field of low-carbon environmental protections and supply chains.