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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.

The waste-to-energy (WtE) project plays a significant role in the sustainable development of urban environments. However, the inherent “Not in my backyard” (NIMBY) effect presents substantial challenges to site selection decisions. While effective public participation is recognized as a potential solution, research on incorporating it into site selection decision-making frameworks remains limited. This paper aims to establish a multi-attribute group decision-making framework for WtE project site selection that considers public participation to enhance public satisfaction and ensure project success.

Firstly, based on consideration of public demand, a WtE project site selection decision indicator system was constructed from five dimensions: natural, economic, social, environmental and other supporting conditions. Next, the Combination Ordered Weighted Averaging (C-OWA) operator and game theory were applied to integrate the indicator weight preferences of experts and the public. Additionally, an interactive, dynamic decision-making mechanism was established to address the heterogeneity among decision-making groups and determine decision-maker weights. Finally, in an intuitive fuzzy environment, an “acronym in Portuguese of interactive and multi-criteria decision-making” (TODIM) method was used to aggregate decision information and evaluate the pros and cons of different options.

This study develops a four-stage multi-attribute group decision-making framework that incorporates public participation and has been successfully applied in a case study. The results demonstrate that the framework effectively handles complex decision-making scenarios involving public participation and ranks potential WtE project sites. It can promote the integration of expert and public decision-making preferences in the site selection of WtE projects to improve the effectiveness of decision-making. In addition, sensitivity and comparative analyses confirm the framework’s feasibility and scientificity.

This paper provides a new research perspective for the WtE project site selection decision-making, which is beneficial for public participation to play a positive role in decision-making. It also offers a valuable reference for managers seeking to effectively implement public participation mechanisms.

Ensuring the safety of structures is important. However, when a structure possesses both an implicit performance function and an extremely small failure probability, traditional methods struggle to conduct a reliability analysis. Therefore, this paper proposes a reliability analysis method aimed at enhancing the efficiency of rare event analysis, using the widely recognized Relevant Vector Machine (RVM).

Drawing from the principles of importance sampling (IS), this paper employs Harris Hawks Optimization (HHO) to ascertain the optimal design point. This approach not only guarantees precision but also facilitates the RVM in approximating the limit state surface. When the U learning function, designed for Kriging, is applied to RVM, it results in sample clustering in the design of experiment (DoE). Therefore, this paper proposes a FU learning function, which is more suitable for RVM.

Three numerical examples and two engineering problem demonstrate the effectiveness of the proposed method.

By employing the HHO algorithm, this paper innovatively applies RVM in IS reliability analysis, proposing a novel method termed RVM-HIS. The RVM-HIS demonstrates exceptional computational efficiency, making it eminently suitable for rare events reliability analysis with implicit performance function. Moreover, the computational efficiency of RVM-HIS has been significantly enhanced through the improvement of the U learning function.

This study investigates the relationship between digitalization and agricultural carbon intensity from 2006 to 2021.

Utilizing panel data from 30 provinces in China from 2006 to 2021, this study employs a threshold and a spatial Durbin model to investigate the relationship between digitalization and agricultural carbon intensity. In addition, a heterogeneity analysis was conducted to understand variations across regions.

The study used threshold models and spatial Durbin models to reveal that in agricultural production, digitalization can significantly reduce the carbon emission intensity of planting and livestock production. Through the threshold effect, it was found that the effectiveness of digitization in curbing emissions varies by region and is influenced by the level of urbanization, with the inhibitory effect being: western > central > eastern. In addition, through spatial analysis, it was found that the impact of digitalization on carbon emission intensity has significant spatial effects, presenting a “high-high” and “low-low” clustering pattern. Moreover, through the Durbin model, digitization exhibits a significant negative spatial impact on the planting industry, and the development of the local digitalization can significantly reduce the carbon emission intensity of the planting industry in neighboring areas. There is a significant positive spatial effect on livestock production, and the development of local digitalization will, to some extent, increase the carbon emission intensity of livestock production in neighboring areas. This study underscores the critical importance of digitalization in reducing agricultural carbon emissions and highlights the necessity for tailored digital strategies that consider regional characteristics and urbanization levels.

This study shows the critical importance of digitalization in reducing agricultural carbon emissions and highlights the necessity for tailored digital strategies that consider regional characteristics and urbanization levels.

This paper pioneers the investigation of the spatial impact of digitalization on agricultural carbon emissions using provincial-level panel data and classifies agriculture into planting and livestock production. This study contributes to the literature by filling the research gap and enhancing our understanding of the relationship between digitalization and environmental sustainability in rural areas.

During metro construction using the shield method, the construction process's complexity, the construction environment's variability, and other factors can easily lead to tunnel construction accidents. This paper aims to explore the interconnections between risk factors and related accident types, as well as the risk chain formed between risk factors, and to analyze the key risk factors and vulnerabilities in shield tunnel construction through empirical data.

Based on the social network analysis theory, the connection of various risk factors in subway shield tunnel construction is explored, and the mechanism of multiple risk factors is studied. Through literature analysis, articles on safety risk factors in metro shield tunnel construction are organized and studied, and the identified safety risk factors can comprehensively reflect the significant risks that need to be concerned in metro shield tunnel construction.

The results show that a small world characterizes the SNA network of safety risk factors for metro shield tunnel construction: The frequency of association between the five risk factors “unsafe behavior,” “site management,” “safety supervision and inspection,” “safety education system” and “safety protection” is higher than that of other factors. Only a few risks, such as “site management,” “safety supervision and inspection,” and “rapid response capability,” directly lead to accidents. In addition, risk factors such as the “safety education system” and “safety protection” will indirectly cause unsafe behaviors of construction personnel.

During construction, the probability of occurrence of risk factors may vary with the construction phase and area and is not considered in this paper. In addition, although this paper identifies, determines and analyzes the risk factors affecting the safety of metro shield tunnel construction, including the importance of each risk factor and the connection between them, more detailed information before and after the accident could not be obtained based on the accident investigation report alone. Therefore, future research can collect the same accident case from more sources to obtain more information.

The theory of accident causation has been improved at the theoretical level. The identified safety risk factors can comprehensively reflect the significant risks that need to be paid attention to in metro shield tunnel construction. From a practical point of view, the results of the study provide a basis for the rational control of the risk factors in the construction of subway shield tunnels, which can help guide practitioners to do a good job of risk prevention before the construction of metro shield tunnels and reduce the probability of related accidents.

This study expands the application of social network analysis in the field of subway tunnel construction risk, quantitatively analyzes the key risk factors and vulnerabilities in shield method tunnel construction and proposes policy recommendations for future metro tunnel construction safety management.

Environmental, social and governance (ESG) performance has received significant attention around the world. Could robust ESG performance become a new advantage for supporting companies’ outward foreign direct investment (OFDI) in emerging markets? Prior studies have not articulated the nexus between ESG performance and OFDI. This paper aims to conduct both theoretical and empirical work to clarify the effect, especially the mechanisms of ESG performance on companies’ OFDI.

Using the data of A-share listed companies in China from 2010 to 2020, this paper empirically tests the effect and the mechanisms of ESG performance on companies’ OFDI.

Firstly, robust ESG performance increases the likelihood of companies engaging in OFDI and also augments the scale of such investments. Within the realm of ESG, environmental performance, social performance and governance performance all play important roles in fostering OFDI. Secondly, strong ESG performance promotes OFDI by enhancing the competitive edge and alleviating financial constraints. Also, environmental performance, social performance and governance performance individually contribute to supporting competitiveness and mitigating financial constraints. Thirdly, the effect of ESG performance on OFDI is particularly pronounced for companies targeting developed countries, those operating in heavily polluting sectors and those with significant institutional investor presence.

This study advances the applicability of the stakeholder theory in the realm of firm internationalization. Moreover, the findings of this paper provide new strategies for promoting the OFDI of companies in emerging market economies.

The number of construction dispute cases has surged in recent years. The effective exploration and management of risks associated with construction contracts helps to directly enhance the overall project performance. The existing approaches to identify the risks associated with construction project contracts have a heavy reliance on manual review techniques, which are inefficient and highly restricted by personnel experience. The existing intelligent approaches only work for the contract query and storage. Hence, it is necessary to improve the intelligence level for contract risk management. This study aims to propose a novel method for the intelligent identification of risks in construction contract clauses based on natural language processing.

This proposed method can formalize the linguistic logic and semantic information of contract clauses into multiple triples and transform the structural processing results of general clauses in a construction contract into rights and interests rules for risk review. In addition, the core semantic information of special clauses in a construction contract, rights and interests rules are used for semantic conflict detection. Finally, this study achieves the intelligent risk identification of construction contract clauses.

The method is verified by selecting several construction contracts that had been applied in engineering contracting as a corpus. The results showed a high level of accuracy and applicability of the proposed method.

This novel method can identify the risks in contract clauses with complex syntactic structures and realize rule extension according to the semantic relation network of the ontology. It can support efficient contract review and assist the decision-making process in contract risk management.

Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to provide a comprehensive review of damage analysis methods at both the material and structural levels.

This study provides an overview of multiscale damage analysis of engineering structures, including its definition and significance. Current status of damage analysis at both material and structural levels is investigated, by reviewing damage models and prediction methods from single-scale to multiscale perspectives. The discussion of prediction methods includes both model-based simulation approaches and data-driven techniques, emphasizing their roles and applications. Finally, summarize the main findings and discuss potential future research directions in this field.

In the material level, damage research primarily focuses on the degradation of material properties at the macroscale using continuum damage mechanics (CDM). In contrast, at the mesoscale, damage research involves analyzing material behavior in the meso-structural domain, focusing on defects like microcracks and void growth. In structural-level damage analysis, the macroscale is typically divided into component and structural scales. The component scale examines damage progression in individual structural elements, such as beams and columns, often using detailed finite element or mesoscale models. The structural scale evaluates the global behavior of the entire structure, typically using simplified models like beam or shell elements.

To achieve realistic simulations, it is essential to include as many mesoscale details as possible. However, this results in significant computational demands. To balance accuracy and efficiency, multiscale methods are employed. These methods are categorized into hierarchical approaches, where different scales are processed sequentially, and concurrent approaches, where multiple scales are solved simultaneously to capture complex interactions across scales.

This study aims to explore the relationship between managerial ownership and environmental innovation, particularly focusing on the impact of minority shareholder protection within the context of China’s A-share listed companies.

The study employs a fixed effect model over a decade-long sample, analysing secondary data from nonfinancial Chinese A-share firms. The two-stage least squares (2SLS) method is adopted to address endogeneity concerns.

The results demonstrate a significant positive influence of managerial ownership on environmental innovation, suggesting that top managers who have a say in the boardroom are inclined towards sustainable development. The presence of minority shareholders' protection positively moderates this relationship, underlining their roles in fostering environmentally friendly development. The subsample analysis showed that these relationships vary between state-owned enterprises (SOEs) and non-SOEs. It also differs between heavily and lightly polluting industries, which indicates that it is not enough to just have internal self-management, and more external pressure is necessary in heavily polluting industries.

Our study underscores the importance for managers to recognize the potential of aligning their ownership interests with environmental objectives. Companies can enhance their commitment to sustainability by fostering an internal environment that supports minority shareholder rights.

This study specifically focuses on the role of top managers and minority shareholders, providing new empirical evidence on how their influence can drive sustainable development initiatives. It is also among the few studies that differentiate between firm characteristics and pollution intensity, which provides valuable insights into how the impact of managerial ownership and minority shareholder protection varies across different contexts.

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.