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The paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.

First, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.

The paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.

The paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

Construction and Demolition (C&D) Waste Management (WM) poses significant challenges in Sri Lanka, contributing to environmental degradation and resource depletion. To address these issues, this study explores the application of Circular Economy (CE) strategies in minimising waste generation and optimising resource utilisation in Sri Lankan construction industry. The research focuses on the construction and building renovation and use and operate stages of the building project life cycle, recognising their significance in waste generation and resource consumption.

The research employed a qualitative approach, utilising the Delphi technique through three rounds of expert interviews. Seventeen experts were involved in the first round, followed by fifteen in the second round, and twelve in the final round. The collected data was analysed using manual content analysis methods.

The research findings revealed fifteen C&D WM issues in the construction and building renovation stage in Sri Lanka, along with suitable strategies to overcome each of them. Similarly, eight C&D WM issues were identified for the use and operate stage of the building, and corresponding strategies were provided to address each issue. By adopting CE strategies such as modular design and material reuse, construction projects can optimise the project's timeline, cost, and quality factors. These strategies enable efficient resource allocation, reduce waste generation, and contribute to the overall sustainability of the project. The impact of CE strategies on mitigating these issues within the project management iron triangle was also discussed.

This paper entails delving into how construction, building renovation, and operation stages of a building's life cycle intersect with CE strategies, which profoundly influence operational efficiency and long-term sustainability. By incorporating principles such as energy efficiency, water conservation, and circular product design, the paper illuminates how these strategies facilitate decreased energy usage, enhanced resource management, and diminished waste production throughout the building's lifespan.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

The principal objective of this current research is to explore and test an underlying mechanism to solve the inconsistent relationship between supervisors’ developmental feedback and employee creativity. This study also tests the moderating role of absorptive capacity in fully understanding the relationship between two constructs.

A time-lagged survey was utilized to collect data from 317 employees working at information technology (IT) organizations in Vietnam. This research conducted a hierarchical regression analysis to examine the hypotheses.

This research found that employees’ operational skills fully mediated the link between supervisors’ developmental feedback and employees’ creative performance. Moreover, absorptive capacity positively moderated the relationship between supervisors’ developmental feedback and employees’ operational skills as well as the relationship between employees’ operational skills and employee creativity.

This research is one of the first papers to discuss the mediating role of employees’ operational skills in associating supervisors’ developmental feedback with employee creativity in response to the calls of previous studies. To fully comprehend the indirect impact of supervisors' developmental feedback on workers' creative performance, this research also examines the moderating influence of absorptive capacity at the individual level.

This paper aims to identify the optimal forming angle for the selective laser melting (SLM) process and evaluate the mechanical properties of the SLM-formed GH3536 alloy in the aero-engine field.

Forming the samples with optimized parameters and analyzing the microstructure and properties of the block samples in different forming angles with scanning electron microscope, XRD, etc. so as to analyze and reveal the laws and mechanism of the block samples in different forming angles by SLM.

There are few cracks on the construction surface of SLM formed samples, and the microstructure shows columnar subgrains and cellular subgrains. The segregation of metal elements was not observed in the microstructure. The pattern shows strong texture strength on the (111) crystal plane. In the sample, the tensile strength of 60° sample is the highest, the plasticity of 90° forming sample is the best, the comprehensive property of 45° sample is the best and the fracture mode is plastic fracture. The comprehensive performance of the part is the best under the forming angle of 45°. To ensure the part size, performance and support structure processing, additional dimensions are added to the part structure.

In this paper, how to make samples with different forming angles is described. Combined with the standard of forged GH3536 alloy, the microstructure and properties of the samples are analyzed, and the optimal forming angle is obtained.

In response to the issue of traditional algorithms often falling into local minima or failing to find feasible solutions in manipulator path planning. The purpose of this paper is to propose a 3D artificial moment method (3D-AMM) for obstacle avoidance for the robotic arm's end-effector.

A new method for constructing temporary attractive points in 3D has been introduced using the vector triple product approach, which generates the attractive moments that attract the end-effector to move toward it. Second, distance weight factorization and spatial projection methods are introduced to improve the solution of repulsive moments in multiobstacle scenarios. Third, a novel motion vector-solving mechanism is proposed to provide nonzero velocity for the end-effector to solve the problem of limiting the solution of the motion vector to a fixed coordinate plane due to dimensionality constraints.

A comparative analysis was conducted between the proposed algorithm and the existing methods, the improved artificial potential field method and the rapidly-random tree method under identical simulation conditions. The results indicate that the 3D-AMM method successfully plans paths with smoother trajectories and reduces the path length by 20.03% to 36.9%. Additionally, the experimental comparison outcomes affirm the feasibility and effectiveness of this method for obstacle avoidance in industrial scenarios.

This paper proposes a 3D-AMM algorithm for manipulator path planning in Cartesian space with multiple obstacles. This method effectively solves the problem of the artificial potential field method easily falling into local minimum points and the low path planning success rate of the rapidly-exploring random tree method.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.

This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.

This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.

This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.

This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.

The purpose of this study is to investigate the benefits and challenges of implementing circular economy (CE), as well as shed light on the influence of procurement strategy in CE implementation in construction projects.

A qualitative research approach with abductive reasoning was adopted. The empirical data were collected from the construction industry in the United Arab Emirates (UAE).

The results reveal that clients, consultants and contractors have limited awareness, knowledge and motivation to implement CE in construction projects. The absence of incentives to design projects following CE principles, lack of involvement of contractors and suppliers, non-use of materials that use CE principles and current procurement strategies are the main challenges for the implementation of CE in the UAE.

Previous research offers limited knowledge on CE and its implementation in construction projects particularly from a procurement strategy perspective. The findings of the study provide new knowledge of the benefits, challenges and role of procurement strategy for implementing CE. It is suggested that collaborative and partnering-based procurement methods are needed to facilitate the effective implementation of CE.

This paper aims to explore UAE's transition towards circular economy (CE) through construction and demolition waste (CDW) management in the pre-construction stage. The extent of circularity is assessed by five key aspects of CE, such as policies and strategic frameworks, design for waste prevention, design for disassembly or deconstruction, use of prefabricated elements and CDW management plans.

Multiple case studies were conducted in the context of the Dubai construction industry (UAE). Three significant and unique construction projects were selected as the cases. Semi-structured interviews were carried out to collect data, and the thematic analysis technique and NVIVO 12 software were used for data analysis.

Findings reveal several positive initiatives towards CE in the UAE context; yet it is identified that the transition is still at the initial stage. Selected case studies, the best-case scenarios of UAE (i.e. influential cases), demonstrated adequate measures in relation to four key CE aspects out of five. For instance, (a) policies and strategic frameworks such as lean standards, green building standards and standards developed by the local authorities, (b) design for waste prevention (e.g. adherence to the 3R principle, and construction planning with BIM), (c) use of prefabricated elements and application of innovative construction technologies (e.g. 3DPC, DfMA) and (d) CDW management planning such as 3R principle were evident. However, the selected cases hardly showcase designing for disassembly or deconstruction.

The existing CDW practices are mostly conventional, as most constructions in UAE are procured through conventional building materials and methods. Therefore, there is a necessity of encouraging CE principles in CDW management. Even though the transition towards CE was evident in four key CE aspects out of five, the UAE construction industry has yet to adopt more effective CE-based CDW management practices to accelerate the circularity. Hence, it is necessary to enforce standard waste management guidelines, including the 3R principle, to standardise CDW management in UAE and encourage construction practitioners to adhere to CE principles.

The findings of this study provide valuable insights for decision-making processes around CDW management towards a CE. This paper contributes to the literature by bridging the CE concept with CDW management in the pre-construction stage. The study provides insights for industry practitioners for planning CE in terms of policies and strategic frameworks, CDW management planning, construction planning and application of innovative construction technologies.