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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.

This study explores the revolutionary potential of merging building information modeling (BIM) and ultra-high performance concrete (UHPC) in prefabricated shell building design, aiming to redefine the architectural landscape.

The study employs a mixed-methods approach, combining quantitative analysis of structural performance data with qualitative case studies of real-world applications. Specific methods include finite element analysis (FEA) for assessing structural integrity and interviews with industry experts to gather insights on practical implementation.

The integration of BIM and UHPC enables the design of structures that are efficient, sustainable and architecturally innovative. Key findings include improved load-bearing capacity, reduced material usage and enhanced design flexibility.

The study focuses on technical aspects, with limited exploration of economic or regulatory factors. Future research could address these areas to provide a more comprehensive understanding.

The findings offer valuable insights for architects, engineers and construction professionals, demonstrating how BIM and UHPC can enhance the efficiency, sustainability and aesthetic appeal of prefabricated shell building designs.

The adoption of BIM and UHPC in prefabricated shell building design promotes the creation of robust and eco-friendly built environments, contributing to societal well-being through enhanced sustainability and reduced carbon footprints.

This study provides a novel perspective on the synergistic use of BIM and UHPC, offering justifications through empirical data and expert testimonials that highlight the unique advantages of this integration in modern construction.

The present research aims to explore the influence of enterprise social media (ESM) features on employee agility through knowledge sharing and hiding. ESM strain was taken as a moderator in the association among knowledge sharing, knowledge hiding and employee agility. Communication visibility theory is used to underpin the hypothesized research model.

Data is collected from 374 Chinese employees working in different companies. A structural equation modeling (SEM) is applied using AMOS software 21.0.

Findings illustrate that ESM features namely analysis, tracking and communication are significantly connected to knowledge sharing and negatively to knowledge hiding. Knowledge sharing is significantly related to employee agility while knowledge hiding is negatively linked to employee agility. Further, ESM strain strengthens the negative connection between knowledge hiding and employee agility. However, ESM strain has an insignificant moderating effect in the connection between knowledge sharing and employee agility.

The current research has practical implications for ESM users and ESM-enabled organizations. For individuals who use ESM to promote knowledge within an organization, this study highlights the important functioning of ESM features that include analysis, tracking and communication. For managers, this study suggests the use of ESM features to promote knowledge sharing and inhibit knowledge hiding.

The current research investigates how ESM features are associated with employee agility in the workplace. This study proposes that knowledge sharing and knowledge hiding are the mediators of the effect path between ESM features and employee agility. Moreover, the present study extends to ESM literature by highlighting the moderating role of ESM strain using communication visibility theory as a theoretical lens. Lastly, this study quantifies the benefits of ESM features to help organizations train agile employees.

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 objective of this research is to put forward a novel closed-loop circular economy (CE) approach to forecast the sustainability of supply chains (SCs). We provide a practical and real-world CE framework to improve and fill the current knowledge gap in evaluating sustainability of SCs. Besides, we aim to propose a real-life managerial forecasting approach to alert the decision-makers on the future unsustainability of SCs.

It is needed to develop an integrated mathematical model to deal with the complexity of sustainability and CE criteria. To address this necessity, for the first time, network data envelopment analysis (NDEA) is incorporated into the dynamic data envelopment analysis (DEA) and artificial neural network (ANN). In general, methodologically, the paper uses a novel hybrid decision-making approach based on a combination of dynamic and network DEA and ANN models to evaluate sustainability of supply chains using environmental, social, and economic criteria based on real life data and experiences of knowledge-based companies so that the study has a good adaptation with the scope of the journal.

A practical CE evaluation framework is proposed by incorporating recyclable undesirable outputs into the models and developing a new hybrid “dynamic NDEA” and “ANN” model. Using ANN, the sustainability trend of supply chains for future periods is forecasted, and the benchmarks are proposed. We deal with the undesirable recycling outputs, inputs, desirable outputs and carry-overs simultaneously.

We propose a novel hybrid dynamic NDEA and ANN approach for forecasting the sustainability of SCs. To do so, for the first time, we incorporate a practical CE concept into the NDEA. Applying the hybrid framework provides us a new ranking approach based on the sustainability trend of SCs, so that we can forecast unsustainable supply chains and recommend preventive solutions (benchmarks) to avoid future losses. A practicable case study is given to demonstrate the real-life applications of the proposed method.

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.