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The 6D pose estimation is a crucial branch of robot vision. However, the authors find that due to the failure to make full use of the complementarity of the appearance and geometry information of the object, the failure to deeply explore the contributions of the features from different regions to the pose estimation, and the failure to take advantage of the invariance of the geometric structure of keypoints, the performances of the most existing methods are not satisfactory. This paper aims to design a high-precision 6D pose estimation method based on above insights.

First, a multi-scale cross-attention-based feature fusion module (MCFF) is designed to aggregate the appearance and geometry information by exploring the correlations between appearance features and geometry features in the various regions. Second, the authors build a multi-query regional-attention-based feature differentiation module (MRFD) to learn the contribution of each region to each keypoint. Finally, a geometric enhancement mechanism (GEM) is designed to use structure information to predict keypoints and optimize both pose and keypoints in the inference phase.

Experiments on several benchmarks and real robot show that the proposed method performs better than existing methods. Ablation studies illustrate the effectiveness of each module of the authors’ method.

A high-precision 6D pose estimation method is proposed by studying the relationship between the appearance and geometry from different object parts and the geometric invariance of the keypoints, which is of great significance for various robot applications.

Mega construction projects (megaprojects) require technological innovation cooperation (TIC) to address complex construction demands and the interests of multiple stakeholders. Although TIC has been extensively discussed at the firm level, a significant gap remains in understanding megaprojects at the project level. This paper aims to identify TIC’s influencing factors and transmission paths and discuss stakeholders’ TIC mechanisms at the project level.

Based on case analysis, expert interviews, literature analysis and the Delphi method, this paper identifies the influencing factors of TIC in megaprojects at the project level. A structural system of these influencing factors is constructed by interpretive structural modeling (ISM), developing various mechanisms for TIC from bottom to top. The Matriced’ Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) method validates the driving forces and dependencies of the influencing factors, clarifying their roles and positions within the system. Additionally, the TIC mechanism is constructed.

The research findings identify 26 influencing factors categorized into four hierarchical levels: cooperative relationships, cooperative behavior, cooperative performance and technological innovation risks. Regarding direct factors, resource sharing affects goal congruence and communication effectiveness in megaprojects, affecting TIC’s satisfaction and trust. Most factors exist in the middle layer, and bridging the upper and lower levels depends on stakeholder collaboration. The root factors in the independent group significantly impact TIC, including policy circumstances, high technical requirements and limited site conditions. Addressing these issues influences improvements in other factors. The development of a digital resource-sharing platform, the enhancement of innovation incentives, the optimization of benefit distribution mechanisms and the improvement of risk-sharing mechanisms are essential for the effective operation of the TIC mechanism.

This study contributes to identifying and classifying challenges and opportunities in TIC. It explores transmission paths for enhancing TIC and presents strategies for successfully implementing and delivering megaprojects.

The study’s purpose is to understand how online communities, thanks to their knowledge-sharing potential, can help to achieve the principles of a human-centered society. The social capital theory is applied to understand knowledge contribution and knowledge sharing in online communities.

A qualitative approach based on a single case study of an international online community is adopted.

The case study highlights how each social capital facet unfolds within the online community to model efficient knowledge exchange among members. The developed social capital generates benefits at three interconnected system levels: micro (individuals), meso (companies), and macro (society).

The paper makes several contributions to the literature on Society 5.0, social capital theory, and knowledge management by bringing the needed empirical evidence on how to exploit online digital technologies to generate the benefits associated with Society 5.0. It also demonstrates that social capital theory is a valuable theoretical lens through which to explain how knowledge-sharing and exchange mechanisms in online communities contribute to shaping a human-centered society.

Drawing on the stakeholder theory, this study aims to empirically analyse the impact of platform enterprises’ corporate social responsibility (CSR) announcements on corporate stock market value. This study also estimates the moderating effect of stakeholder orientation and responsibility categories of CSR announcements, the platform enterprise type and the degree of CSR disclosure.

The event study method is used to analyse the change in stock market value of 191 CSR announcements from 137 Chinese platform enterprises. In addition, a case analysis is presented for two platform enterprises with the best practices to validate and complement study findings.

CSR announcements improve platform enterprises’ stock market value. Specifically, CSR announcements responding to platform enterprises’ external stakeholders, and CSR announcements with economic responsibility, have obvious positive impacts on stock market value. Furthermore, the maker platform’s CSR announcement has a more positive impact on stock market value than the exchange platform.

To the best of the authors’ knowledge, this study is the first attempt to identify the link between platform enterprises’ CSR announcements and stock market performance by empirical evidence, and it contributes to new knowledge of operating and evaluating platform enterprises’ CSR.

This paper aims to present a new trajectory scheduling method to generate a smooth and continuous trajectory for a hybrid machining robot.

The trajectory scheduling method includes two steps. First, a G3 continuity local smoothing approach is proposed to smooth the toolpath. Then, considering the tool/joint motion and geometric error constraints, a jerk-continuous feedrate scheduling method is proposed to generate the trajectory.

The simulations and experiments are conducted on the hybrid robot TriMule-800. The simulation results demonstrate that this method is effectively applicable to machining trajectory scheduling for various parts and is computationally friendly. Moreover, it improves the robot machining speed and ensures smooth operation under constraints. The results of the S-shaped part machining experiment show that the resulting surface profile error is below 0.12 mm specified in the ISO standard, confirming that the proposed method can ensure the machining accuracy of the hybrid robot.

This paper implements an analytical local toolpath smoothing approach to address the non-high-order continuity problem of the toolpath expressed in G code. Meanwhile, the feedrate scheduling method addresses the segmented paths after local smoothing, achieving smooth and continuous trajectory generation to balance machining accuracy and machining efficiency.

This study seeks to investigate role of the coronavirus disease 2019 (COVID-19) pandemic on clean energy stocks for the United States for the period 21 January 2020–16 August 2021.

At the empirical stage, the Fourier-augmented vector autoregression approach has been used.

According to the empirical results, the response of the clean energy stocks to the feverish sentiment, lockdown stringency, oil volatility, dirty assets, and monetary policy dies out within a short period of time. In addition, the authors find that there is a unidirectional causality from the feverish sentiment index and the lockdown stringency index to the clean energy stock returns; and from the monetary policy to the clean energy stocks. At the same time, there is a bidirectional causality between the lockdown stringency index and the feverish sentiment index. The empirical findings can be helpful to both practitioners and policy-makers.

Among the COVID-19 variables used in this study is a new feverish sentiment index, which has been constructed using principal component analysis. The importance of the feverish sentiment index is that it allows us to examine the impact of the aggregate level of fear in the economy on clean energy stocks.

The purpose of this study is to address the welding demands within large steel structures by presenting a global spatial motion planning algorithm for a mobile manipulator. This algorithm is based on an independently developed wall-climbing robot, which comprises a four-wheeled climbing mobile platform and a six-degree-of-freedom robotic manipulator, ensuring high mobility and operational flexibility.

A convex hull feasible domain constraint is developed for motion planning in the mobile manipulator. For extensive spatial movements, connected sequences of convex polyhedra are established between the composite robot’s initial and target states. The composite robot’s path and obstacle avoidance optimization problem are solved by constraining the control points on B-spline curves. A dynamic spatial constraint rapidlye-xploring random trees-connect (RRTC) motion planning algorithm is proposed for the manipulator, which quickly generates reference paths using spherical spatial constraints at the manipulator’s end, eliminating the need for complex nonconvex constraint modeling.

Experimental results show that the proposed motion planning algorithm achieves optimal paths that meet task constraints, significantly reducing computation times in task conditions and shortening operation times in non-task conditions.

The algorithm proposed in this paper holds certain application value for the realization of automated welding operations within large steel structures using mobile manipulator.

This study aims to investigate the potential of wood as a water-lubricated bearing material, determine the factors influencing the water-lubricated properties of wood and identify suitable alternatives to Lignum vitae.

Three resource-abundant wood species, Platycladus orientalis, Cunninghamia lanceolata and Betula platyphylla, were selected, and their properties were compared with those of Lignum vitae. The influencing mechanism of the tribological properties of different woods under water lubrication was thoroughly analyzed, in conjunction with the characterization and testing of mechanical properties, micromorphology and chemical composition.

The findings reveal that the mechanical properties and inclusions of wood are the primary factors affecting its tribological properties, which are significantly influenced by the micromorphology and chemical composition. The friction experiment results demonstrate that Lignum vitae exhibits the best tribological properties among the four wood species. The tribological properties of Platycladus orientalis are comparable to those of Lignum vitae, being only 17.1% higher. However, it is noted that higher mechanical properties can exacerbate the wear of the grinding pair.

The originality of this study lies in the combination of friction experiments and wood performance tests to identify the factors contributing to the superior water lubrication performance of wood, thereby guiding the application and improvement of different wood types in water-lubricated bearings.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0284/

The present study scrutinizes the relative performance of various near-wall treatments coupled with two-equation RANS models to explore the turbulence transport mechanism in terms of the kinetic energy budget in a plane wall jet and the significance of the near-wall molecular and turbulent shear, to select the best combination among the models which reveals wall jet characteristics most efficiently.

A two-dimensional steady incompressible plane wall jet in a quiescent surrounding is simulated using ANSYS-Fluent solver. Three near-wall treatments, namely the Standard Wall Function (SWF), Enhanced Wall Treatment (EWT) and Menter-Lechner (ML) treatment coupled with Realisable, RNG and Standard k-e models and also the Standard and Shear-Stress Transport (SST) k-ω models are employed for this investigation.

The ML treatment slightly overestimated the budget components on an outer scale, whereas the k-ω models strikingly underestimated them. In the buffer layer at the inner scale, the SWF highly over-predicts turbulent production and dissipation and k-ω models over-predict dissipation. Appreciably accurate inner and outer scale k-budgets are observed with the EWT schemes. With a sufficiently resolved near-wall mesh, the Realisable model with EWT exhibits the mean flow, turbulence characteristics and turbulence energy transport even better than the SST k-ω model.

Three distinct near-wall strategies are chosen for comparative performance analysis, focusing not only on the mean flow and turbulence characteristics but the turbulence energy budget as well, for finding the best combination, having potential as a viable and low-cost alternative to LES and DNS for wall jet simulation in industrial application.

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.