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The structure and protective effect of Al-coated Nd-Fe-B magnets before and after grain boundary diffusion were studied to explore the feasibility of improving the corrosion resistance of Nd-Fe-B magnets by Al coating and Al grain boundary diffusion.

The Al coating was deposited on sintered Nd-Fe-B magnets by magnetron sputtering, and then the Al-coated Nd-Fe-B magnets were put into the vacuum tube furnace for grain boundary diffusion process. The influence of Al coating and grain boundary diffusion process on the corrosion resistance of Nd-Fe-B magnets was investigated using electrochemical tests.

Results showed that the Al coating thickness increases, the corrosion current density of Al-coated magnets first increases and then decreases with increasing coating time. The Al coating particles transform from small millet shaped particles to equiaxed polygonal particles, and finally to big millet shaped particles with increasing coating time. The diffusion temperature has little effect on the corrosion potential, and the corrosion current density of Al-diffused magnets after grain boundary diffusion is much higher than that of Al-coated magnets before grain boundary diffusion. The corrosion potential and corrosion current density of magnets first increase, and then decrease with increasing tempering temperature.

As for high intrinsic coercivity Nd-Fe-B magnets, Poor preparation of Al coatings can result in Al coatings lacking protective properties, and the Al coating should be used cautiously as the surface protective coating of magnets. Grain boundary diffusion leads to the disappearance of Al coating, and reduces the corrosion resistance of Nd-Fe-B magnets, while tempering treatment can improve the corrosion resistance of Al-diffused magnets.

Recently, due to the practicability in several domains, generative adversarial network (GAN) has successfully been adopted in the field of natural language generation (NLG). The purpose of this paper focuses on improving the quality of text and generating sequences similar to human writing for several real applications.

A novel model, GAN², is developed based on a GAN with dual adversarial architecture. We train the generator by an internal discriminator with a beam search technique to improve the quality of generated sequences. Then, we enhance the generator with an external discriminator to optimize and strengthen the learning process of sequence generation.

The proposed GAN² model could be utilized in widespread applications, such as chatbots, machine translation and image description. By the proposed dual adversarial structure, we significantly improve the quality of the generated text. The average and top-1 metrics, such as NLL, BLEU and ROUGE, are used to measure the generated sentences from the GAN² model over all baselines. Several experiments are conducted to demonstrate the performance and superiority of the proposed model compared with the state-of-the-art methods on numerous evaluation metrics.

Generally, reward sparsity and mode collapse are two main challenging issues when adopt GAN to real NLG applications. In this study, GAN² exploits a dual adversarial architecture which facilitates the learning process in the early training stage for solving the problem of reward sparsity. The occurrence of mode collapse also could be reduced in the later training stage with the introduced comparative discriminator by avoiding high rewards for training in a specific mode. Furthermore, the proposed model is applied to several synthetic and real datasets to show the practicability and exhibit great generalization with all discussed metrics.

The purpose of this paper is to investigate the accurate monitoring and assessment of steel bar corrosion in concrete based on deep learning multi-sensor information fusion method. The paper addresses the issue of traditional corrosion assessment models relying on sufficient data volume and low evaluation accuracy under small sample conditions.

A multi-sensor integrated corrosion monitoring equipment for reinforced concrete is designed to detect corrosion parameters such as corrosion potential, current, impedance, electromagnetic signal and steel bar stress, as well as environmental parameters such as internal temperature, humidity and chloride ion concentration of concrete. To overcome the small amount of monitoring data and improve the accuracy of evaluation, an improved Siamese neural network based on the attention mechanism and multi-loss fusion function is proposed to establish a corrosion evaluation model suitable for small sample data.

The corrosion assessment model has an accuracy of 98.41%, which is 20% more accurate than traditional models.

Timely maintenance of buildings according to corrosion evaluation results can improve maintenance efficiency and reduce maintenance costs, which is of great significance to ensure structural safety.

The corrosion monitoring equipment for reinforced concrete designed in this paper can realize the whole process of monitoring inside the concrete. The proposed corrosion evaluation model for reinforced concrete based on Siamese neural network has high accuracy and can provide a more accurate assessment model for structural health testing.

The impact of enterprise social media (ESM) on employees is not always beneficial. The constant connectivity of ESM can trigger emotional issues, threatening employees' psychological states. This study aims to explore the potential risks of ESM usage on thriving at work by examining how ESM usage affects thriving and for whom this relationship is amplified.

Drawing on Affect Event Theory, this study proposes a research model for investigating the underlying mechanisms and boundary conditions of the negative impacts of different ESM usage patterns on employees' thriving at work. This study employed Smart-PLS 4.0 with structural equation modeling to analyze multi-wave survey data from 255 employees, testing the proposed model.

The results indicate the following: (1) ESM usage triggers employees' workplace fear of missing out (WFoMO), increasing their online vigilance and consequently hindering their thriving at work. (2) The impact of ESM usage on employees' WFoMO and thriving at work varies with their ambition levels, with high-ambition employees being more susceptible to negative effects and (3) Different types of ESM usage exert distinct effects on employees.

This study substantiates the potential negative impact of ESM usage on employees' thriving at work, contributing to the literature on the “dark side” of ESM usage and thriving at work. This study confirms the critical mediating role of emotion, offering a novel theoretical perspective on understanding the mechanisms linking ESM usage and its outcomes. Additionally, this study identifies the moderating role of employee ambition, complementing the boundary conditions of ESM usage.

Promoting technological innovation is important to address the complexity of major engineering challenges. Technological innovations include short-term innovations at the project level and long-term innovations that can enhance competitive advantages. The purpose of this study is to develop an incentive mechanism for the public sector that considers short-term and long-term efforts from the private sector, aiming to promote technological innovation in major engineering projects.

This study constructs an incentive model considering the differences in short-term and long-term innovation efforts from the private sector. This model emphasizes the spillover effect of long-term efforts on current projects and the cost synergy effect between short-term and long-term efforts. It also explores the factors influencing the optimal incentive strategies for the public sector and innovation strategies for the private sector.

The results indicate that increasing the output coefficient of short-term and long-term efforts and reducing the cost coefficient not only enhance the innovation efforts of the private sector but also prompt the public sector to increase the incentive coefficient. The spillover effect of long-term innovation efforts and the synergy effect of the two efforts are positively related to the incentive coefficient for the public sector.

This research addresses the existing gap in understanding how the public sector should devise incentive mechanisms for technological innovation when contractors acting as the private sector are responsible for construction within a public-private partnership (PPP) model. In constructing the incentive mechanism model, this study incorporates the private sector's short-term efforts at the project level and their long-term efforts for sustained corporate development, thus adding considerable practical significance.

This study aims to evaluate the activation of personality traits (PTs) as a means of explaining the emergence of intrapreneurial behaviour (IB) in engineers, particularly in response to goal-setting freedom (GSF) cues. Hence, this study proposes and empirically assesses the theoretical model underpinning trait activation theory (TAT), enriching its application by integrating goal-setting theory.

The proposed theoretical model was evaluated through a cross-sectional survey of 296 engineers in Pakistan’s textile industry. Testing of hypotheses using analysis of the data was done using partial least squares structural equation modelling (PLS-SEM).

The obtained results revealed a positive relationship between the PTs of extraversion, conscientiousness, openness to experience and emotional stability with IB, while agreeableness was not found to have a significant relationship. The situational cue of GSF activated these traits, validating the theoretical model and demonstrating the contextual influence of autonomy on personality expression.

Granting engineers greater GSF can significantly enhance their IB. Organisations should foster environments that support autonomy to drive innovation, leveraging the intrinsic motivation and creativity of their engineers. This approach can lead to increased innovation, proactive problem-solving and competitive advantage.

This study contributes to the literature by extending TAT within the textile industry, illustrating how GSF acts as a situational moderator. It bridges gaps in understanding sector-specific dynamics and underscores the role of autonomy in activating PTs to foster innovation, proactiveness and risk-taking behaviours in organisational contexts.

The purpose of this study is to comprehensively evaluate the impact of the prefabrication rate on greenhouse gas (GHG) emissions and sustainability in prefabricated construction. In addition, it aims to identify the optimal prefabrication rate threshold that can promote the transformation of the construction industry toward more environmentally friendly practices.

This study uses an interdisciplinary methodology that combines emergy analysis with an extended input-output model to develop a GHG emission accounting model tailored for prefabricated buildings. The model assesses various construction schemes based on different rates of prefabrication and uses the emergy phase diagram from ecological economics to quantify the sustainability of these schemes.

This study indicates that within a prefabrication rate threshold of 61.27%–71.08%, a 5% increase in the prefabrication rate can significantly reduce emissions by approximately 36,800 kg CO₂(e). However, emissions begin to rise when the prefabrication rate exceeds this threshold. The case analysis identifies steel, concrete and electricity as the primary sources of GHG emissions, suggesting strategies for optimizing their usage and promoting the adoption of clean energy.

This study represents a novel tool for assessing the environmental impact and sustainability of prefabricated buildings. It offers scientific guidance for the construction industry’s environmental protection and sustainable development strategies, thereby contributing to a transition toward more environmentally friendly practices.

This study aims to investigate the micro mechanism of macro rheological characteristics for composite modified asphalt.Grey relational analysis (GRA) was used to analyze the correlation between macro rheological indexes and micro infrared spectroscopy indexes.

First, a dynamic shear rheometer and a bending beam rheometer were used to obtain the evaluation indexes of high- and low-temperature rheological characteristics for asphalt (virgin, SBS/styrene butadiene rubber [SBR], SBS/rubber and SBR/rubber) respectively, and its variation rules were analyzed. Subsequently, the infrared spectroscopy test was used to obtain the micro rheological characteristics of asphalt, which were qualitatively and quantitatively analyzed, and its variation rules were analyzed. Finally, with the help of GRA, the macro-micro evaluation indexes were correlated, and the improvement efficiency of composite modifiers on asphalt was explored from rheological characteristics.

It was found that the deformation resistance and aging resistance of SBS/rubber composite modified asphalt are relatively good, and the modification effect of composite modifier and virgin asphalt is realized through physical combination, and the rheological characteristics change with the accumulation of functional groups. The correlation between macro rutting factor and micro functional group index is high, and the relationship between macro Burgers model parameters and micro functional group index is also close.

Results reveal the basic principle of inherent-improved synergistic effect for composite modifiers on asphalt and provide a theoretical basis for improving the composite modified asphalt.

This study aims to investigate the impact of firms’ digital orientation (FDO) on corporate green innovation (CGI) among Chinese firms, examining the effects of financial constraint as the mediator and exploring heterogeneous effects across different firm contexts.

Using a sample of 28,697 firm-year observations from Chinese A-share listed companies (2008–2021), we employ a novel multidimensional measure of FDO derived from textual analysis of corporate annual reports. CGI is quantified using patent-based metrics. We utilize fixed-effects panel data models as benchmark regression to quantify FDO’s impact on CGI. Later, we utilize two-stage least squares, alternate measure for core explanatory variable, alternate as well as lead measures for explained variable and propensity score matching to tackle concerns for potential endogeneity.

Our results unveil a substantial positive connection between FDO and CGI. This connection is facilitated through the alleviation of financial constraints. Furthermore, heterogeneity analysis shows that the impact of FDO on CGI is more pronounced for state-owned enterprises, firms in areas with lower financial technology development and politically connected firms.

Our findings suggest that managers should view FDO as a strategic posture that can drive sustainable innovation, not just as a technological imperative. Policymakers should consider the role of FDO when designing policies to promote CGI, particularly in less-developed regions.

This study extends current understanding by: (1) Employing a comprehensive multidimensional measure of FDO that goes beyond the existing technologically focused digital transformation matrices. (2) Identifying financial constraints as a key mediating mechanism in the FDO–CGI relationship. (3) Revealing heterogeneous effects across different firm contexts, providing nuanced insights into how institutional and environmental factors moderate this relationship.

This research focuses on exploring a psychological mechanism between abusive supervision and job engagement via job insecurity and job alternative to understand how frontline construction workers' perceived abusive supervision impacts on their work engagement.

This study draws on the conservation of resources theory (COR) to postulate that workers' job insecurity acts as a mediation between abusive supervision and work engagement, and the perception of job alternative moderates the impact of abusive supervision on job insecurity. This study empirically tests survey data from 357 frontline construction workers and adopts partial least square structural equation modeling (PLS-SEM) to test the proposed model.

This paper proved the dark-side of abusive supervision, dual character of job insecurity and moderating role of job alternative. The game-changing mediation effects of job insecurity were examined, which suggested that work engagement would be higher when abusive supervision stimulates stronger job insecurity. Furthermore, the result indicated that the relationship between abusive supervision and job insecurity should be weaker when job alternative is high.

This research suggests construction project managers (CPMs) must take steps such as enhancing supervisors' emotional management skills to control frequency of abuse and thus lessen compromising engagement. Moreover, overall understanding frontline workers' job insecurity is meaningful for better construction performance management.

This research contributes to the disputed leadership style (abusive supervision) and positive psychology knowledge (work engagement) in construction projects by unveiling the psychosocial mechanism (game-changing effects of job insecurity) of construction frontline workers positive engaged feeling. This study highlights the function of personal and job resources in process of resource conservation.