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This study was aimed at clarifying the post-fire shear strength of self-drilling screws and the load-bearing capacity of single overlapped screwed connections using steel sheets and self-drilling screws. The self-drilling screws for shear tests were made of high-strength, martensitic-stainless and austenitic stainless-steel bars.

Shear loading tests were conducted on self-drilling screws to obtain basic information on post-fire shear strength. Tensile tests were conducted on the screwed connections to examine the transition of failure modes depending on the test temperature after experiencing the heating and cooling procedures.

The post-fire shear strengths and reduction factors of self-drilling screws of each steel grade were quantified. Furthermore, heated temperature-dependent sheet bearing failure, net sheet failure and screw shear failure modes were observed for the screwed connections.

The transition of the failure modes of the screwed connection could be explained using the equations of the post-fire shear strength proposed in this study. The basic experimental data required to evaluate the post-fire shear strength of screws were obtained.

The purpose of this paper is to examine the influence of intelligent manufacturing on audit quality and its underlying mechanism as well as the variation in this influence across different types of organizations.

This research utilizes a difference-in-differences (DID) method to examine how enterprises that apply intelligent manufacturing choose auditors and impact their audit work. The study is based on 15,228 observations of Chinese-listed A-shares from 2011 to 2020.

(1) There is a strong correlation between intelligent manufacturing and audit quality. (2) This positive correlation is statistically significant only in state-owned enterprises (SOEs), those that have steady institutional investors and where the roles of the CEO and chairman are distinct. (3) Enterprises that have implemented intelligent manufacturing are more inclined to employ auditors who possess extensive industry expertise. The auditor's industry expertise plays a crucial role in ensuring audit quality. (4) The adoption of intelligent manufacturing also leads to higher audit fees and longer audit delay periods.

This paper validates the beneficial impact of intelligent manufacturing on improving corporate governance. In addition, it is recommended that managers prioritize the involvement of skilled auditors with specialized knowledge in the industry to ensure the high audit quality and the transparency of information in intelligent manufacturing enterprises.

This study builds upon previous research that has shown the importance of artificial intelligence in enhancing audit procedures. It contributes to the existing body of knowledge by examining how enterprise intelligent manufacturing systems (IMS) enhance audit quality. Additionally, this study provides valuable information on how to improve audit quality in the field of intelligent manufacturing by strategically selecting auditors based on resource dependency theory.

While the impact of digitalization on businesses has been extensively studied, the influence of digitalization on marketing outcomes in private enterprises has not received sufficient attention. The current study aims to examine how and when digitalization affects international marketing decisions in the context of private enterprises.

This study employs data from a survey of Chinese private enterprises conducted in 2020, which constitutes the world's largest dataset of its kind. Nearly 19,000 samples were included in the study. Additionally, we also incorporate supplementary data on digitalization in the Chinese region. Employing various methods, this study empirically and robustly examines the proposed research framework within the context of Chinese private enterprises.

Based on the resource-based view and agency theory, this paper found that digitalization can positively impact private enterprises’ direct and indirect international marketing decisions. Furthermore, we introduce the inclusion of innovation capacity and board governance as moderators in the model and find that board governance attenuates the influence of digitalization on international marketing decisions, while innovation capacity enhances the impact of digitalization on direct international marketing but diminishes its effect on indirect international marketing.

This study advances the understanding of the impact of digitalization on international marketing in private enterprises, thereby addressing the gap in the limited focus on digitalization in private enterprises. It also demonstrates how private enterprises effectively utilize digitalization to gain marketing advantages in the international market.

As an ancient art form, embroidery has strong practicality and artistic value. However, current embroidery style migration models produce images with unclear textures and a lack of stitch detail. So, in this paper, we propose a cyclic consistent embroidery style migration network with texture constraints, which is called Texture Cycle GAN (TCGAN).

The model is based on the existing Cycle GAN network with an additional texture module. This texture module is implemented using a pre-trained Markovian adversarial network to synthesize embroidery texture features. The overall algorithm consists of two generative adversarial networks (for style migration) and the Markovian adversarial network (for texture synthesis).

Qualitative and quantitative experiments show that, compared with the existing convolutional neural network style transfer algorithm, the introduction of the texture-constrained embroidery style transfer model TCGAN can effectively learn the characteristics of style images, generate digital embroidery works with clear texture and natural stitches and achieve more realistic embroidery simulation effects.

By improving the algorithm for image style migration and designing a reasonable loss function, the generated embroidery patterns are made more detailed, which shows that the model can improve the realism of embroidery style simulation and help to improve the standard of embroidery craftsmanship, thus promoting the development of the embroidery industry.

(1) The shear lag effect and its additional deflection contribution to composite beams based on spatial grid elements were presented. (2) A refined spatial grid element analysis method that can simultaneously obtain the internal forces, displacements and stresses of various parts of a composite beam.

A refined spatial grid element analysis method.

The proposed method can directly obtain the internal forces and displacements of the joints of the composite beam roof, floor and web.

To comprehensively comprehend the mechanical behavior of double-girder steel plate composite girder bridge structures and facilitate refined analysis, this paper introduces a refined spatial grid element analysis model applicable to both the global and local domains.

This study introduces the heritage city risk dimension of the urban rail transit (URT) projects. It aims to identify the risk factors affecting URT projects within the unique context of heritage-rich cities, exploring their interrelation and evaluating critical factors.

The research adopts a multi-case exploratory study to identify the unique challenges faced by URT projects in heritage-rich environments, followed by a comprehensive risk assessment framework integrating Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Analytic Network Process (ANP) and Risk Interaction Network (RIN) analysis to assess identified risks in the context of Kathmandu Valley. Additionally, a risk response action is simulated using RIN analysis.

About 16 risk factors were identified from the case studies and evaluated using the proposed risk assessment methodology. The study reveals a highly interconnected risk environment, with heritage impact-related factors exerting the strongest causative influence on cost and social engagement factors. Community opposition (R8) shows the highest betweenness centrality, indicating its central position in risk propagation across the network. Cost-related risk, social demand contingency (R2) ranked as the most critical. Simulations of a targeted risk avoidance strategy showed that addressing only three key high-betweenness centrality factors (R5, R8 and R15) reduced overall risk interactions by 46%, simplifying the risk network, reducing project complexity and improving manageability.

The findings emphasize that project managers, urban planners and policymakers should integrate heritage preservation concerns when planning and executing URT projects in heritage-rich cities. Moreover, the research highlights that effective community engagement serves as a key strategy for reducing risk propagation and plays a crucial role in overall project risk management.

The study contributes to the underexplored context of URT projects in heritage-rich cities, providing a comprehensive risk management framework for identifying and assessing project risks intersecting with urban development imperatives and heritage conservation objectives.

This study aims to investigate the mechanical and tribological behavior of 70Mn steel with different laser re-melted textured patterns.

Laser surface re-melting (LSR) was used to manufacture various textured patterns (i.e. line, grid and mixed) on both the original and heat-treated 70Mn steel plates. The micro-hardness, microstructure, tensile strength, yield strength, elongation, coefficients of friction (COF) and worn morphologies were characterized to evaluate the impact of different textured patterns on the overall performance.

The results show that re-melted unit exhibited the highest surface hardness on the subsurface. The increase in surface hardness of the re-melted unit for the heat-treated 70Mn steel samples was much lower than that of the original ones. The re-melted textured patterns did not improve the tensile strength, yield strength and elongation of either original or heat-treated 70Mn steel samples. The re-melted textured patterns effectively reduced the average COFs of heat-treated 70Mn steel samples, but increased friction of the non-heat-treated samples.

This study provides valuable insights into enhancing the mechanical properties and tribological characteristics of 70Mn steel, particularly in the automotive, heavy machinery and high-load application sectors. These industries have stringent requirements for durability and friction control, and the findings of this research are expected to effectively extend the lifespan of mechanical components.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0443/

With the accumulation of theoretical research and practical experience in the field of garment production research, it is imperative to methodically analyze and reflect on the achievements that have been made. This review aims to systematically map the academic landscape of research articles on garment production, elucidate the evolutionary trajectory of this discipline, identify emerging research frontiers and provide insights into its prospects.

Based on the Web of Science core database, 307 research articles were systematically analyzed by CiteSpace software. The study employed bibliometric and thematic analyses to offer in-depth insights into the dynamics and evolution of research on garment production.

Results reveal that keyword analysis emphasizes the significance of topics such as apparel assembly line, lean production, circular economy, fuzzy logic, global production networks, social sustainability and supply chain management in garment production research. Citation analysis demonstrates that articles related to environmental impact, supply chain management, production process and production technology constitute the knowledge base and core of garment production research. Eight principal research themes emerge: customized garment production, production technology, quality assurance, equipment, production lines, supply chain management, environmental impact and social and human impact. Future research hotspots will focus more on sustainable, intelligent and digital clothing production.

The findings systematically sort out the hotspots and trends in garment production, establish knowledge structures and display them through intuitive representations. The rich insights set the stage for the development of garment production and provide future guidance for theoretical research.

Based on the collective agentic perspective of social cognitive theory and supplemented by social support theory, this study aims to investigate how the entrepreneurial collective efficacy of entrepreneurial teams impacts team effectiveness. Specifically, this study hypothesizes that entrepreneurial collective efficacy is a critical factor affecting effectiveness through the mechanism of instrumental support to varying extents, depending on team size.

The hypotheses were tested on a Chinese sample of new venture teams (N teams = 81; N individuals = 335). A multisource questionnaire that included questions for team leaders and other team decision makers was designed. The data on entrepreneurial collective efficacy (six items) and instrumental support (four items) comprised the aggregate responses from all team members; the data on team effectiveness (seven items) was reported by team leaders.

The findings indicate that entrepreneurial collective efficacy is positively associated with team effectiveness through instrumental support, especially in small teams.

This study makes important contributions to the research related to exploring in uncertain environments (entrepreneurship) how (instrumental support) entrepreneurial collective efficacy impacts team effectiveness, as well as more particularly under what conditions (team size), all within the specific context of collectivistic cultures (China).

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.