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A physically-based elasto-viscoplastic constitutive model is proposed to examine the size effects of the precipitate and blocks on the creep for martensitic heat-resistant steels with both the dislocation creep and diffusional creep mechanisms considered.

The model relies upon the initial dislocation density and the sizes of M₂₃C₆ carbide and MX carbonitride, through the use of internal variable based governing equations to address the dislocation density evolution and precipitate coarsening processes. Most parameters of the model can be obtained from existing literature, while a small subset requires calibration. Based on the least-squares fitting method, the calibration is successfully done by comparing the modeling and experimental results of the steady state creep rate at 600° C across a wide range of applied stresses.

The model predictions of the creep responses at various stresses and temperatures, the carbide coarsening and the dislocation density evolution are consistent with the experimental data in literature. The modeling results indicate that considerable effect of the sizes of precipitates occurs only during the creep at relatively high stress levels where dislocation creep dominates, while the martensite block size effect happens during creep at relatively low stress levels where diffusion creep dominates. The size effect of M₂₃C₆ carbide on the steady creep rate is more significant than that of MX precipitate.

The present study also reveals that the two creep mechanisms compete such that at a given temperature the contribution of the diffusion creep mechanism decreases with increasing stress, while the contribution of the dislocation creep mechanism increases.

The purpose of this paper is to design a new trajectory error compensation method to improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction motion.

A trajectory error compensation method based on admittance-extended Kalman filter (AEKF) error fusion for human–exoskeleton interaction control. The admittance controller is used to calculate the trajectory error adjustment through the feedback human–exoskeleton interaction force, and the actual trajectory error is obtained through the encoder feedback of exoskeleton and the designed trajectory. By using the fusion and prediction characteristics of EKF, the calculated trajectory error adjustment and the actual error are fused to obtain a new trajectory error compensation, which is feedback to the knee exoskeleton controller. This method is designed to be capable of improving the trajectory tracking performance of the knee exoskeleton and enhancing the compliance of knee exoskeleton interaction.

Six volunteers conducted comparative experiments on four different motion frequencies. The experimental results show that this method can effectively improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction.

The AEKF method first uses the data fusion idea to fuse the estimated error with measurement errors, obtaining more accurate trajectory error compensation for the knee exoskeleton motion control. This work provides great benefits for the trajectory tracking performance and compliance of lower limb exoskeletons in human–exoskeleton interaction movements.

This study explores how establishing an agile supply chain ecosystem (ASCE) during the COVID-19 pandemic impacts three listed auto firms’ financial and customer performance in China. Drawing on the theory of supply chain resilience, responsiveness and restoration (3R capabilities), we collect, mine and analyze unstructured social media data from Dongfeng Motor Corporation (DFM), BYD Company Limited (BYD) and SAIC Motor Corporation Limited (SAIC), all of which have production plants in Wuhan.

Data mining was conducted on 3,528 comments involving DFM, BYD and SAIC from 190 uploaders in the stages of “Early COVID-19 Period (January 23-May 31, 2020)” and “Enhanced Preparedness and Vaccine Launch Period (September 8, 2020–January 15, 2021)”. Our conceptual model of an ASCE was tested by adopting structural equation modelling and verified by the DFM case study.

The study finds that the 3R capabilities of the supply chain have a positive effect on establishing an automotive agile supply chain ecosystem. Establishing the automotive agile supply chain ecosystem has promoted corporate financial performance and customer performance, but the mechanism of action presents a delayed feature.

This study refines the definitions and application of the 3Rs concerning an ASCE in the automotive industry. Through text mining technology and statistical analysis methods, we uncover and discuss the impact of COVID-19 on the performance of three auto companies in China and examine how ASCE establishment plays a role. Constructing an ASCE provides a theoretical basis and realistic approach for the long-term and healthy development of the automotive supply chain.

According to the resource dependence theory and peer effect theory, this paper explores the mechanism of the focal firm’s knowledge coherence on technological distance among its supply chain partners.

Based on patent data of 301 Information Technology and Communication Services firms over 18 years from 1998 to 2015, this paper conducts panel regression analysis.

Empirical results find the positive relationship between the focal firm’s knowledge coherence and technological distance among its supply chain partners. Furthermore, conclusions indicate that the focal firm’s degree centrality and ego network density negatively and positively moderate this relationship respectively.

The findings of this paper contribute to the existing research in supply chain network innovation and offers guidelines to choose partners with varying technological compositions according to their knowledge bases and network structure characteristics.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

Human resources transformation is an essential concept that significantly changes how organizations manage their personnel in an ever-changing business environment. This research intends to explore the potential of artificial intelligence (AI) technologies to facilitate human resource (HR) transformation in automobile companies in China.

The research employs qualitative approaches such as interviews and case studies to gain insights and viewpoints from key stakeholders. The interviews were semi-structured and carried out in person, enabling an in-depth investigation of their experiences and perspectives. The duration of each interview ranged from 30 to 40 minutes.

The study explored three main themes: employee acceptability, career growth and performance, using system thinking theory. The findings highlight the various effects of AI technology on HR changes in automobile businesses. First, AI helps increase employee approval by simplifying activities and improving user experience. Second, it greatly impacts professional advancement by providing tailored learning and skill-building chances. Third, AI integration has a favorable effect on organizational performance indicators such as efficiency, agility and adaptability.

This research's findings significantly contribute to scholars and practitioners engaged in HR reforms. Comprehending the subtle impacts of AI technology on employee acceptability, career advancement and organizational effectiveness helps guide strategic decision-making. Furthermore, these results emphasize the need to connect AI-based HR initiatives with overall corporate goals to enhance impact and efficiency

This research provides new insights into how AI technologies interface with HR transformation. The study offers a thorough understanding of intricate dynamics and identifies new possibilities for using AI in HR practices via system thinking theory. The research enhances academic discussion on HR transformation in the age of AI-driven innovation.

In recent years, the global focus has increasingly shifted toward the adoption of electric vehicles (EVs) due to growing concerns about environmental sustainability and the imperative of reducing greenhouse gas emissions. The transportation sector, a significant contributor to air pollution and climate change, faces increasing pressure to embrace EVs as a solution. However, the resistance exhibited by customers toward adopting new technology poses a substantial obstacle to the widespread adoption of EVs. Drawing on the link between theory of reasoned action (TRA) and self-congruity theory, this research aims to determine the factors that affect the customer intention toward EV.

The research conducts a questionnaire collecting 950 respondents from the Egyptian market. The research used primary quantitative data from online and self-administered questionnaires.

The findings indicated that green trust, price sensitivity and reliability have a positive impact on customer’s intention. However, self-image congruence was not affecting customer intention. For the moderating role of financial self-efficacy, it is affecting the relationship between price sensitivity and customer’s purchase intentions toward EV.

This research will expand the theory by conceptualizing its abstract notions through research variables and implementing them in the Egyptian market. Furthermore, it links the two distinct theories. This knowledge can be utilized by policymakers and stakeholders to expedite the adoption of EVs in the Egyptian market.

This study presents a conceptual framework for managers and policymakers about the factors that affect the customer to buy EVs, since the international organizations emphasize eco-friendly transportation systems.

Using perspectives from the technology affordance and social capital theories, this study aims to unpack the process through which platform-enabled co-development unfolds in supply chain contexts. Specifically, it explores how innovation outcomes can be fostered through platform affordances and supply chain relationship (SCR) capital.

The paper integrates literature on digital platforms, SCRs and co-development to produce an integrative framework, developing propositions on the relationships among digital platforms, SCR capital and innovation outcomes.

The authors identify affordances for distinctive strategic use of platforms: value co-creation, relationship building and strategic learning. The authors discuss ways in which each affordance contributes to the advances in SCR capital, thus altogether enabling focal firms to orchestrate and integrate internal and external resources to attain incremental and radical innovation.

Based on the proposed research framework, further empirical studies can use quantitative data to measure the relationship between affordances and SCR capital and use longitudinal case studies to explore how affordances and SCR capital evolve to provide more fine-grained and contextualised information in different research settings.

This paper sheds light on how the relation between the adoption of digital platforms and SCR capital shapes digitally enabled service co-development. The authors provide an alternative explanation of resource integration in platform-mediated supply chain contexts and enrich the related literature on how digital platforms can maximise value from introducing ambidextrous innovation by leveraging internal and external resources.

Based on a stimulus–organism–response theory, the purpose of this study is to empirically examine structural equation model linking personal involvement, positive emotions, tourist satisfaction and destination loyalty among sport tourists to a mega sport event. Moreover, moderating effects of place attachment on the relation between the aforementioned variables were investigated.

The validities and reliabilities of the measures were investigated through confirmatory factor analysis, Cronbach's alpha and correlation analysis. A structural equation modelling with maximum likelihood estimation was tested to analyze the relationships among the research variables using 383 participants.

The results revealed positive associations among stimulus (personal involvement), organism (positive emotions) and response (tourist satisfaction and destination loyalty). Moreover, the authors found moderating effect of place attachment on the relationships between personal involvement and positive emotions, personal involvement and tourist satisfaction and tourist satisfaction and destination loyalty.

This study holds the potential to aid destination managers in acquiring a more profound comprehension of how personal involvement contributes to elicit positive emotions, keep tourists satisfied and build destination loyalty as well as demonstrating the moderating roles of place attachment. However, generalizing the findings to alternative contexts presents a formidable challenge. Enhancing the applicability of these findings could be achieved through prospective research endeavors that explore visitors in diverse cities spanning various continents.

The study contributed to the literature by providing empirical evidence that personal involvement evokes positive emotions while also plays significant role in improving satisfaction and loyalty. Given the importance of experiences in sport tourism, this study also confirmed the role of positive emotions on tourist satisfaction and destination loyalty. Additionally, this study examined the moderating effect of place attachment, which has not been investigated in sport tourism context.

This paper aims to explore the underlying mechanisms and boundary conditions through which equipment manufacturing enterprises can capture market value from digital transformation, with a specific focus on the roles of knowledge search and knowledge recombination.

This study uses a double fixed-effects model to test the hypotheses, using a unique data set of “firm-year” observations from 739 publicly listed equipment manufacturing companies in China, spanning the period from 2018 to 2022.

Digital transformation drives market value creation in equipment manufacturing enterprises through both breakthrough knowledge recombination (BKR) and progressive knowledge recombination (PKR). In addition, the analysis of marginal conditions reveals that diversified knowledge search serves as a substitute for digital transformation in promoting BKR, while also positively moderating the relationship between digital transformation and PKR.

Grounded in the knowledge-based view theoretical framework, this study introduces the novel concepts of BKR and PKR and systematically examines how digital transformation impacts market value in equipment manufacturing enterprises.