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The purpose of this study was to validate the feasibility of the proposed microstructure-based model by comparing the simulation results with experimental data. The study also aimed to investigate the relationship between the orientation of graphite flakes and the failure behavior of the material under compressive loads as well as the effect of image size on the accuracy of stress–strain behavior predictions.

This paper presents a microstructure-based model that utilizes the finite element method (FEM) combined with representative volume elements (RVE) to simulate the hardening and failure behavior of ferrite-pearlite matrix gray cast iron under uniaxial loading conditions. The material was first analyzed using optical microscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) to identify the different phases and their characteristics. High-resolution SEM images of the undeformed material microstructure were then converted into finite element meshes using OOF2 software. The Johnson–Cook (J–C) model, along with a damage model, was employed in Abaqus FEA software to estimate the elastic and elastoplastic behavior under assumed plane stress conditions.

The findings indicate that crack initiation and propagation in gray cast iron begin at the interface between graphite particles and the pearlitic matrix, with microcrack networks extending into the metal matrix, eventually coalescing to cause material failure. The ferritic phase within the material contributes some ductility, thereby delaying crack initiation.

This study introduces a novel approach by integrating microstructural analysis with FEM and RVE techniques to accurately model the hardening and failure behavior of gray cast iron under uniaxial loading. The incorporation of high-resolution SEM images into finite element meshes, combined with the J–C model and damage assessment in Abaqus, provides a comprehensive method for predicting material performance. This approach enhances the understanding of the microstructural influences on crack initiation and propagation, offering valuable insights for improving the design and durability of gray cast iron components.

With the rapid low-carbon transformation in China, the industrial approach and labor structure of mining enterprises are undergoing constant changes, leading to an increasing psychological dilemma faced by coal miners. This study aims to reveal the relationship and mechanism of factors influencing the psychological dilemma of miners, and to provide optimal intervention strategies for the safety and sustainable development of employees and enterprises.

To effectively address the complex issue of the psychological dilemma faced by miners, this study identifies and constructs five-dimensional elements, comprising 20 indicators, that influence psychological dilemmas. The relational mechanism of action of factors influencing psychological dilemma was then elucidated using an integration of interpretive structural modeling and cross-impact matrix multiplication.

Industry dilemma perception is a “direct” factor with dependent attributes. The perceptions of management response and relationship dilemmas are “root” factors with driving attributes. Change adaptation dilemma perception is a “susceptibility” factor with linkage attributes. Work dilemma perception is a “blunt” factor with both dependent and autonomous attributes.

The aforementioned findings offer a critical theoretical and practical foundation for developing systematic and cascading intervention strategies to address the psychological dilemma mining enterprises face, which contributes to advancing a high-quality coal industry and efficient energy development.

In today’s business landscape, drawing upon the critical role of environmental sustainability, this study investigates the intricate relationship between green human resource management practices (GHRMP), big data analytics capability (BDAC), green competitive advantage (GCA) and environmental performance (EP), further moderated by managerial environmental concern (MEC).

This study employs a quantitative approach using the latest version of SmartPLS 4 version 4.0.9.6 on a data sample of 467 participants representing a diverse range of manufacturing SMEs. Data were collected from managers and directors using a structured questionnaire and analyzed using structural equation modeling (SEM). This study contributes to the existing knowledge by integrating GHRMP and BDAC within the GCA framework, providing a comprehensive understanding of how these practices enhance SME`s sustainability.

The findings provide valuable insights into the manufacturing sector, aiming to enhance SMEs' green competitive advantage. Implementing GHRMP fosters environmental awareness within the workforce, and building BDAC allows for effectively translating that GHRMP into actionable insights, maximizing the potential for achieving GCA. Furthermore, recognizing MEC’s moderating role strengthens positive environmental outcomes associated with GCA. The findings confirm that GHRMP and BDAC are valuable resources and key drivers contributing to competitive advantage in sustainability of enterprises.

For SMEs, our findings suggest that strategically integrating GHRMP with BDAC not only boosts environmental stewardship but also improves operational efficiency and market positioning. This research outlines actionable steps for SMEs aiming to achieve sustainability targets while enhancing profitability. This research provides actionable insights for SMEs in strategic decision-making and policy formulation, aiding SMEs in navigating the complexities of sustainable development effectively.

This study contributes to the existing knowledge by integrating GHRMP and BDAC within the GCA framework, providing a robust theoretical explanation of how HRM practices and BDAC help SMEs gain green competitiveness. The implication of this study reveals that SMEs implementing and integrating green HRM practices with advanced data analytics are more likely to gain competitive advantage. This study draws theoretical support from the resource-based view (RBV) theory, positing that a firm’s sustainable competitive advantage stems from its unique and valuable resources and capabilities that are difficult for competitors to imitate or substitute.

New product development (NPD) projects are strategically important for firms’ operations but suffer from high failure rates. Leadership is a key factor for project success. However, in contrast to positive project leadership, project managers’ knowledge hiding has received little attention. Drawing on the input-mediator-output (IMO) framework and model of work team resilience, we explored the effect of project managers’ destructive knowledge hiding (i.e. evasive hiding and playing dumb) on project team performance (i.e. efficiency and effectiveness) and the serial indirect effect through team psychological safety and transactive memory systems.

We conducted a time-lagged multiple-sourcing investigation of Chinese high-tech firms and tested the hypotheses using data collected from 105 NPD project teams.

Our findings demonstrated that project managers’ knowledge hiding negatively affects NPD project team performance and indirectly negatively affects transactive memory systems through team psychological safety. Moreover, project managers’ knowledge hiding exerts a negative indirect effect on team performance through team psychological safety and transactive memory systems in serial.

This study contributes to the literature on operations management (OM) by broadening our understanding of the connection between project managers' destructive knowledge hiding and the failure of NPD projects. In providing such insight, it also offers practical guidance for overcoming team-level obstacles arising from project managers' knowledge hiding.

Graded honeycombs are materials that exhibit better energy absorption performance compared to uniform honeycombs without adding additional weight. This paper introduces a novel modularized graded honeycomb into a commercial crash box to improve its crashworthiness.

A modularized graded honeycomb is inserted into a commercial crash box to develop a novel crash box. Finite element analyses are conducted to investigate the crashworthiness. Pareto cumulative influence analysis is conducted to rank the effects of design parameters on crashworthiness. A surrogate model-based multi-objective optimization is carried out to improve energy absorption while limiting the impact peak force. An optimal Pareto solution set is obtained.

Modularized honeycomb-filled crash box outperforms that of its corresponding uniform honeycomb-filled crash box and empty crash box in resisting impact. Pareto cumulative influence analysis reveals that for most crashworthiness indicators, cell-wall thicknesses of crash box tube contribute the most, followed by average relative density and graded coefficient of modularized honeycomb (MH). Graded coefficient contributes nearly 10% on mean force and maximum displacement, but it has insignificant influence on peak force and weight. Optimization results show that the optimal designs can not only absorb more energy but also limit the peak force compared with those of uniform honeycomb-filled crash box.

This paper fills a MH into a commercial crash box to propose a novel crash box and demonstrates the positive impact of modularized design on crashworthiness compared with that of uniform honeycomb-filled crash box. Moreover, modularizing honeycomb does not change the weight of the filler, and thus, the novel crash box would benefit development of crash box with lightweight and excellent energy absorption capacity.

Consumers are assumed to engage in external information search only after exhausting their internal information sources. Guided by the accessibility/diagnosticity and ease-of-retrieval frameworks, and the elaboration likelihood model, the current study investigates this phenomenon.

To test the relationships between internal information accessibility/diagnosticity and the importance of external search, and the moderating role of involvement in these relationships, 308 responses were collected on Amazon MTurk. Then, structural equation modeling was employed to analyze the data.

The analyses showed that while accessibility and diagnosticity of internal information have an impact on external information search, involvement with the product class has a consequential moderating effect on these relationships. In particular, in the low-involvement group, only the diagnosticity of internal information had a negative effect on external information search. On the contrary, in the high-involvement group, only accessibility of internal information had a negative effect.

These findings highlight the possibility of drawing erroneous conclusions resulting from not incorporating involvement, in conjunction with information accessibility and diagnosticity, in the study of the consumer external information search behavior.

The findings also imply that if practitioners aim to prime consumers to engage in external information search, they need to take into account that the effects of internal information's accessibility and diagnosticity on consumers' external search behavior may be different depending on their levels of involvement.

This study's results showed that without considering the moderating effect of involvement, spurious conclusions may be made about the relationships between accessibility and diagnosticity of internal and external information importance. This finding may explain the discrepancy between the accessibility/diagnosticity and ease-of-retrieval frameworks, thus enriching the literature.

A precise estimation of the evolutionary power spectral density (EPSD) of typhoon wind speed is a difficult and significant undertaking in the analysis of turbulence effects on large-expansive structures. A majority of the prevailing EPSD estimation techniques rely on complex signal processing methodologies, such as wavelet decomposition, Hilbert–Huang transformation and time-varying autoregressive moving average (ARMA) model. However, these approaches often pose challenges in terms of comprehensibility and practical implementation for engineers. In light of this issue, the present study introduces a straightforward and effective EPSD estimation method tailored specifically for typhoon wind speed, aiming to facilitate its understanding and application in engineering contexts.

Firstly, the mathematical model of a uniformly modulated non-stationary process is employed to represent the typhoon wind speed. Secondly, the reverse arrangement test serves as an auxiliary tool in conjunction with wavelet transform or empirical mode decomposition, aiding in the determination of the optimal slowly varying mean wind speed. Thirdly, Kernel regression technique is utilized to discern the time-dependent standard deviation of wind speed fluctuations. Finally, the power spectral density (PSD) of wind speed residuals is computed to facilitate the estimation of the EPSD.

Firstly, the reverse arrangement test-assisted approach enables the determination of an optimal time-dependent mean from the candidate results obtained through discrete wavelet transform (DWT) and empirical mode decomposition (EMD). Secondly, the application of the Kernel regression technique facilitates accurate identification of the time-dependent variance from the fluctuating wind speed data. Thirdly, due to the influence of the extreme weather, the Gaussianity of the reduced turbulent fluctuations in typhoon wind is easily disturbed, resulting in the obvious non-Gaussian features.

This paper employs the mathematical model of uniformly modulated non-stationary process to characterize typhoon wind speeds and then proposes a straightforward and efficient method for estimating the EPSD of typhoon wind. The accuracy and efficacy of the presented estimation method are verified using the field-measured wind speed data from Typhoon Rammasun. The proposed EPSD estimation method for typhoon wind exhibits suitability for engineering applications owing to its simplicity and computational efficiency.

The few previous researches on the impact of calf compression garments (CG) on running performance while assessing physiological and perceptual factors. Therefore, this study investigated how the clothing pressure of two types of Calf CG, CG1 and CG2, affects muscle fatigue and activation during running.

Five healthy amateur runners(three female and two male)were recruited for a 30-min running trial. They wear a Calf CG on their right leg (CG group), but not on their left leg(CON group). After obtaining the clothing pressure of Calf CG on the gastrocnemius lateral head (GL), gastrocnemius medial head (GM) and tibialis anterior(TA) of the right leg, surface electromyography (sEMG)of four muscles of GL, GM, TA and rectus femoris (RF) of the left and right legs were measured during running, and heart rate, cardiopulmonary rate, and human RPE were also measured. Blood bleed oxygen before and after the running trial were measured. The root mean square (RMS) of the characteristic values was selected as an index for the analysis of sEMG signals, and the data were analyzed using statistical and computational methods.

The results showed that the indexes of heart rate, blood oxygen, and RPE were significantly increased, indicating that the subjects had reached the fatigue level. The comparison of mean clothing pressure at GL, GM and TA locations reveals that the TA location consistently exhibits the highest pressure for both types of CG. When wearing CG1, the mean clothing pressure at the GL and GM test points is greater than that of CG2(CG1-GL = 0.2059 kPa > CG2-GL = 0.148 kPa; CG1-GM = 0.1633 kPa > CG2-GM = 0.127 kPa). This is attributed to the double-layered fabric on the sides of CG1, which precisely covers the GL and GM areas, thereby resulting in higher mean clothing pressure at these locations compared to CG2. Conversely, the mean clothing pressure at the TA location for CG1 is lower than that for CG2(CG1-TA = 0.3852 kPa < CG2-TA = 0.426 kPa). The pressure exerted by the CG1 on the lower limb test areas has both positive and negative effects, though neither are statistically significant. The pressure exerted by CG2 alleviates fatigue at the directly affected locations GL and GM, but exerts excessive pressure on TA, resulting in a negative effect. Additionally, CG2 pressure alleviates fatigue at the indirectly affected location RF on the same side. Based on the specific clothing pressure data, it is concluded that when the pressure at the GM location is 0.127 kPa, 30 min of running has a fatigue-relieving effect. However, the pressure should not be excessively high, at 0.1633 kPa it exhibits an insignificant adverse effect. At the TA location, a garment pressure mean between 0.3852 and 0.426 kPa does not alleviate fatigue after 30 min of running, and the negative effect becomes more pronounced as the pressure increases. The pressure exerted by the CG at GL, GM, TA and RF locations shows significant changes from the previous time period during the 15–18 min interval after running. Therefore, in the design of CG, attention should be paid to the changes in clothing pressure effects on muscles during this specific time period.

The few previous researches on the impact of calf compression garments (CG) on running performance while assessing physiological and perceptual factors. Therefore, this study investigated how the clothing pressure of two types of Calf CG, CG1 and CG2, affects muscle fatigue and activation during running.

This study examines conditions under which taxes and subsidies designed to mitigate corporate environmental impact positively influence corporate pro-environmental behavior (CPEB) adoption, considering unique dynamics within different industries.

A systematic literature review was conducted on 171 articles. Articles were coded using an inductive grid for comprehensive examination.

Taxes generally positively influence CPEB adoption, but reduced positive or even negative effects can arise. Subsidies, while often facilitating the achievement of environmental goals, variously impact CPEB. Explanations for variations include the level of taxation or subsidy, economic agent affected, subsidy source, nature of subsidy, factors external to tax or subsidy characteristics and conflicting environmental objectives. We suggest research avenues for each aspect, to enhance literature on the influence of tax policies on promoting CPEB.

Beyond general tax-policy considerations, we provide policymakers with recommendations for tax policies designed to promote CPEB.

We examine the distinctive effects of taxes and subsidies on CPEB adoption within diverse industries ((re)manufacturing, agriculture, shipping, automobile, freight transport and power generation). We compare specific effects across industries, and advocate detailed exploration of recurrent elements identified, emphasizing their potential significance in designing taxes and subsidies that promote CPEB.

The 2030 Agenda for Sustainable Development underscores eradicating global poverty (Goal#1) and empowering women and girls (Goal#5) to foster sustainable, inclusive communities (Goal#11). The World Bank recognises female entrepreneurship as a catalyst for global economic growth, poverty reduction and gender equity. This chapter delves into the challenges hindering female entrepreneurship, obstructing poverty alleviation and community sustainability efforts worldwide. Public–private collaborations are crucial to support women in launching start-ups, adopting new technologies, enhancing digital skills and accessing financing in the era of Industry 4.0.

Our focus is on women entrepreneurship in BRICS nations due to their diverse growth trajectories and global economic significance. Employing a qualitative approach, we analyse public and private initiatives promoting female entrepreneurship in BRICS countries. Our findings highlight both commonalities and distinctions in their strategies and policies, implicitly contributing to poverty reduction and social and economic growth. This chapter not only identifies barriers faced by women entrepreneurs but also underscores factors fuelling their ventures. It offers a practical toolkit for scholars, policymakers and practitioners (entrepreneurs and consultants) to devise tailored strategies and actions for local growth and intervention. The study comprises four parts: the introduction, setting the chapter's goal and previewing outcomes; the second part, exploring female entrepreneurship as a key to poverty alleviation and community sustainability; the core, the third part, unveiling in-depth BRICS country analysis; and the conclusion, summarising implications and highlighting avenues for further research.