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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.

Motivated by the significant role of uncertainty in affecting investment decisions and China's economic leadership in Asia, this paper investigates the predictive role of exposure to Chinese economic policy uncertainty at the individual stock level in large Asian markets.

We estimate the monthly uncertainty exposure (beta) for each stock and then employ the portfolio-level sorting analysis to investigate the relationship between the China’s uncertainty exposure and the future returns of major Asian markets over multiple trading horizons. The raw returns of the high-minus-low portfolios are then adjusted using conventional asset pricing models to investigate whether the relationship is explained by common risk factors. Finally, we check the robustness of the portfolio-level results through firm-level Fama and MacBeth (1973) regressions.

Applying portfolio-level sorting analysis, we reveal that exposure to Chinese uncertainty is negatively related to the future returns of large stocks over multiple trading horizons in Japan, Hong Kong and India. We discover this is unexplained by common risk factors, including market, size, value, profitability, investment and momentum, and is robust to the specification of stock-level Fama and MacBeth (1973) regressions.

Our analysis demonstrates the spillover effects of Chinese economic policy uncertainty across the region, provides evidence of China's emerging economic leadership, and offers trading strategies for managing uncertainty risks.

The findings of the study significantly improve our understanding of stock return predictability in Asian markets. Unlike previous studies, our results challenge the leading role of the US by providing a new intra-regional return predictor, namely, China’s uncertainty exposure. These results also evidence the continuing integration of the Asian economy and financial markets. However, contrary findings for some Asian markets point toward certain market-specific features. Compared with market-level research, our analysis provides deeper insights into the performance of individual stocks and is of particular importance to investors and other market participants.

The purpose is to investigate how to create a new premium new energy vehicle brand.

This study employed a two-stage design. Firstly, a single-case study of NIO was undertaken to execute a thematic analysis, from which propositions were proposed and a theoretical model was constructed. Subsequently, quantitative data were collected through the questionnaire method to empirically test the model developed in the first stage.

NIO creates great user experience through four aspects: product, service, digital touchpoints and lifestyle. Functional experience is shaped by product and digital touchpoints, while emotional experience is affected by service and lifestyle. NIO wins extremely high user satisfaction through great user experience. User satisfaction is affected by both functional and emotional experience. Taking extremely satisfied users as the core, NIO leverages word-of-mouth recommendations to increase brand awareness and build premium brand image, so as to achieve high performance in the long term.

This study contributes to the literature by proposing and testing a theoretical model of creating a new premium new energy vehicle brand. It highlights the significance of emotional factors in the process of creating a new premium brand. It proposes employing the “ripple model” to translate user satisfaction into financial performance. It provides a three-step guide to creating a new premium brand for managers.

The escalating integration of AI tools like ChatGPT within academia poses a critical challenge regarding their impact on faculty members’ and researchers’ academic performance levels. This paper aims to delve into academic performance within the context of the ChatGPT era by exploring the influence of several pivotal predictors, such as academic integrity, academic competence, personal best goals and perceived stress, as well as the moderating effect of ChatGPT adoption on academic performance.

This study uses a quantitative method to investigate the impact of essential variables on academic integrity, academic competence, perceived stress and personal best goals by analysing 402 responses gathered from ResearchGate and Academia.edu sites.

While affirming the established direct positive relationship between academic integrity and performance since adopting AI tools, this research revealed a significant moderating role of ChatGPT adoption on this relationship. Additionally, the authors shed light on the positive relationship between academic competence and performance in the ChatGPT era and the ChatGPT adoption-moderated interaction of competence and performance. Surprisingly, a negative association emerges between personal best goals and academic performance within ChatGPT-assisted environments. Notably, the study underscores a significant relationship between heightened performance through ChatGPT and increased perceived stress among academicians.

The research advocates formulating clear ethical guidelines, robust support mechanisms and stress-management interventions to maintain academic integrity, enhance competence and prioritise academic professionals’ well-being in navigating the integration of AI tools in modern academia.

This research stands out for its timeliness and the apparent gaps in current literature. There is notably little research on the use of ChatGPT in academic settings, making this investigation among the first to delve into how faculty and researchers in education use OpenAI.

This paper aims to present the mechanical design and kinematics of a novel rigid-flexible coupling hybrid robot to develop a promising aeroengine blades in situ repair technology.

According to requirements analysis, a novel rigid-flexible coupling hybrid robot is proposed by combining a three degrees of freedom (DOF) parallel mechanism with a flexible continuum section. Then the kinematics models of both parallel mechanism and flexible continuum section are derived respectively. Finally, based on equivalent joint method, a two-step numerical iterative inverse kinematics algorithm is proposed for the whole robot: (1) the flexible continuum section is equivalently transformed to a 2-DOF spherical joint, thus the approximate analytical inverse kinematic solution can be obtained; (2) the accurate solution is derived by an iterative derivation of both parallel mechanism and flexible continuum section.

To verify structure scheme and the proposed kinematics modeling method, numerical simulations and prototype experiments are implemented. The results show that the proposed kinematics algorithm has sufficient accuracy and computational efficiency in the whole available workspace, that is end-effector position error and orientation error are less than 0.2 mm and 0.01° respectively, and computation time is less than 0.22s.

A novel rigid-flexible coupling hybrid robot for aeroengine blades in situ repair is designed. A two-step numerical iterative inverse kinematics algorithm is proposed for this unique hybrid robots, which has good accuracy and computational efficiency.

The purpose of this study is to examine different paths to overcoming the liability of foreignness. Based on the eclectic paradigm, the authors construct a theoretical framework comprising enterprise nature, location choice, entry mode and internationalization strategy.

The paper uses fuzzy-set qualitative comparative analysis (fsQCA) method to test the framework with data covering 120 multinational Chinese subsidiaries in 34 host in 2019.

The results show that liability of foreignness (LOF) is multiple concurrency, equifinality and asymmetry. When investing in Belt and Road (B&R) countries, non-SEOs can weaken LOF by applying the greenfield mode and resource-seeking strategy, other MNEs can implement a market- or resource-seeking strategy via cross-border M&A to reduce LOF. But when investing in non-B&R countries with a strategic asset-seeking strategy, the LOF is increased. The B&R initiative can reduce the LOF effectively.

The authors construct a general framework to explain the paths of overcoming LOF by bridging the OLI with LOF and introduce fsQCA method into the field of LOF to make up for the shortcoming of existing test method by explaining the influence of more than three factors on LOF.

The proposed model aims to tackle the data quality issues in multivariate time series caused by missing values. It preserves data set integrity by accurately imputing missing data, ensuring reliable analysis outcomes.

The Conv-DMSA model employs a combination of self-attention mechanisms and convolutional networks to handle the complexities of multivariate time series data. The convolutional network is adept at learning features across uneven time intervals through an imputation feature map, while the Diagonal Mask Self-Attention (DMSA) block is specifically designed to capture time dependencies and feature correlations. This dual approach allows the model to effectively address the temporal imbalance, feature correlation and time dependency challenges that are often overlooked in traditional imputation models.

Extensive experiments conducted on two public data sets and a real project data set have demonstrated the adaptability and effectiveness of the Conv-DMSA model for imputing missing data. The model outperforms baseline methods by significantly reducing the Root Mean Square Error (RMSE) metric, showcasing its superior performance. Specifically, Conv-DMSA has been found to reduce RMSE by 37.2% to 63.87% compared to other models, indicating its enhanced accuracy and efficiency in handling missing data in multivariate time series.

The Conv-DMSA model introduces a unique combination of convolutional networks and self-attention mechanisms to the field of missing data imputation. Its innovative use of a diagonal mask within the self-attention block allows for a more nuanced understanding of the data’s temporal and relational aspects. This novel approach not only addresses the existing shortcomings of conventional imputation methods but also sets a new standard for handling missing data in complex, multivariate time series data sets. The model’s superior performance and its capacity to adapt to varying levels of missing data make it a significant contribution to the field.

The study aims to investigate the structural relationship between motivation, involvement, satisfaction and loyalty in the context of hiking tourism in Hong Kong.

This study develops a comprehensive conceptual framework that integrates motivations, satisfaction, loyalty and involvement. A convenience survey of hikers (n = 859) was conducted in Tai Mo Shan Country Park in Hong Kong, from December 2020 to May 2021. The data were analyzed using structural equation modeling.

The results indicated that stimulus–avoidance motive and involvement had direct effects on satisfaction. Stimulus–avoidance, social and competence–mastery motives were correlated with involvement which indirectly affected satisfaction. Loyalty was directly affected by satisfaction and involvement.

This study contributes to the existing literature by filling the research gap in hiking tourism. It not only provides a novel understanding of tourist behavior but also offers valuable implications for improving hiking experiences and meeting the needs of hikers effectively.

While existing literature offers valuable insights into social media knowledge, absorptive capacity, and competitive intelligence, it does not fully address the integrated activities involved in generating valuable knowledge. This study aims to address this gap by investigating how high-tech companies leverage knowledge acquired from social media, mediated through absorptive capacity, to generate valuable competitive intelligence.

A total of 270 valid responses were collected from high-tech company managers through an online survey. PROCESS Model 4, employing 5,000 bootstrap samples and 95% confidence intervals, was used to assess the constructs of interest. Using the Knowledge-Based View, this approach tested the mediating role of companies' absorptive capacity in the association between knowledge acquisition from social media and competitive intelligence processes.

The results indicate that knowledge acquisition from social media, both directly and indirectly (through absorptive capacity), positively influences competitive intelligence processes, including collection and analysis.

Acquiring knowledge from social media does not necessarily lead to superior intelligence in companies. This study helps managers focus on enhancing companies' absorptive capacity for more effective competitive intelligence processes.

This study investigates the role of absorptive capacity in terms of the connection between acquiring knowledge from social media and competitive intelligence. The findings have significant theoretical and managerial implications, providing valuable insights that could help high-tech company managers improve intelligence generation in their companies.

Using a 62,742-ha working forest in New Brunswick, Canada, we examine the benefit and cost of carbon additionality at the landscape level.

The baseline scenario is set to maximize timber profit over a 100-year planning period, whereas the carbon scenario is set to have a 5- or 10-year rotation extension.

At a carbon price of $8/tCO₂e, the benefit of additional carbon sequestration from the working forest cannot offset its cost. For the benefit-cost ratio to be one, the respective break-even price needs to be $21/tCO₂e for the 5-year rotation extension and $25/tCO₂e for the 10-year rotation extension.

This study analyzes the carbon additionality and economics of working forests at the 50–100 thousand hectare scale. Specifically, we examine the change in benefit and cost between a baseline scenario of timber management only and a scenario of rotation extension for both timber and carbon sequestration.