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Two friction models of Fe-Fe and Diamond-like carbon (DLC)-Fe were established by molecular dynamics (MD) method to simulate the friction behavior of traditional fracturing pump plunger and new DLC plunger from atomic scale. This paper aims to investigate the effects of temperature and load on the friction behavior between sealed nitrile butadiene rubber (NBR) and DLC films.

In this study, MD method is used to investigate the friction behavior and mechanism of DLC film on plungers and sealing NBR based on Fe-Fe system and DLC-Fe system.

The results show that the friction coefficient of DLC-Fe system exhibits a downward trend with increasing load and temperature. And even achieve a superlubricity state of 0.005 when the load is 1 GPa. Further research revealed that the low interaction energy between DLC and NBR promoted the proportion of atoms with larger shear strain in NBR matrix and the lower Fe layer in DLC-Fe system to be much lower than that in Fe-Fe system. In addition, the application of DLC film can effectively inhibit the temperature rise of friction interface, but will occur relatively large peak velocity.

In this paper, two MD models were established to simulate the friction behavior between fracturing pump plunger and sealing rubber. Through the analysis of mean square displacement, atomic temperature, velocity and Interaction energy, it can be seen that the application of DLC film has a positive effect on reducing the friction of NBR.

Drawing from resource orchestration theory, this research proposed an integrative model that leverages insights into counter resource constraints and uncertainty in start-up business model innovation (BMI). It investigated the influences of entrepreneurial networks and effectuation on BMI through bricolage in uncertain environments.

The research surveyed 481 start-ups in China. LISREL 8.80 and SPSS 22.0 were employed to test the validity and reliability of key variables, respectively. Additionally, hypotheses were examined through multiple linear regression.

First, entrepreneurial networks and effectuation were positively related to BMI, and combining these two factors improved BMI for start-ups. Second, bricolage contributed to BMI and played mediating roles in translating entrepreneurial networks and effectuation into BMI. Third, environmental uncertainty weakened the linkage between bricolage and BMI.

Future research should replicate the results in other countries because only start-ups in China were investigated in the study, and it is necessary to extend this research by gathering longitudinal data. This research emphasized the mediating effects of bricolage and the moderating influence of environmental uncertainty, and new potential mediating and moderating factors should be explored between resources and BMI.

There are three significant theoretical contributions. First, the findings enrich the literature on the complex antecedents of BMI by combining the impacts of entrepreneurial networks and effectuation. Second, an overarching framework is proposed explaining how bricolage (resource management) links entrepreneurial networks and effectuation and BMI. Third, it demonstrates the significance of environmental uncertainty in the bricolage–BMI linkage, deepening the understanding of the bricolage boundary condition.

While operational carbon (OC) emission reduction strategies have received substantial attention in past literature, very few studies have focused on embodied carbon (EC) emission reduction in the construction industry. Therefore, this study aims at undertaking a scientometric review of strategies to mitigate EC emissions in the construction industry.

Scopus search engine was used to search for articles. VOSViewer software was used for scientometric analysis using science mapping approach. Using a total of 151 documents, keywords, authors, papers and their sources were analysed. Furthermore, scientometric analysis was undertaken comprising co-occurrence of keywords, documents source analysis and author co-citation analysis.

The significant strategies identified to mitigate EC emissions were: offsite manufacturing/use of prefabricated elements, decarbonisation of energy grid, enhanced policies and regulations by governments, construction sector policies and regulations, guidelines for increased use of low EC materials and reuse and recovery of EC construction materials.

This study identifies practical strategies that contribute to reduction of EC emissions.

This study is significant and contributes to the construction industry’s agenda to mitigate greenhouse gas emissions.

Carbon emissions trading is an effective instrument in reducing greenhouse gas emissions. There is a notable scarcity of comprehensive reviews on the modelling techniques within carbon trading research in construction.

This paper reviews 68 relevant articles published in 19 peer-reviewed journals using systematic search. Scientometric analysis and content analysis are undertaken.

Generally, China was the largest contributor to carbon trading research using quantitative models (representing 36% of the total articles). From the results, the modelling techniques identified were multi-objective grasshopper optimisation algorithm; system dynamics; interpretive structural modelling; multi-agent-based model; decision-support model; multi-objective chaotic sine cosine algorithm; optimised backpropagation neural network; sequential panel selection method; Granger causality test; and impulse response analysis. Moreover, the advantages and disadvantages of these techniques were identified. System dynamics was recommended as the most suitable modelling technique for carbon trading in construction.

This study is significant, and through this review paper, practitioners can easily be more familiar with the significant modelling techniques, and this will motivate them to better understand their uses.

Traditional library book recommendation methods are mainly based on association rules and user profiles. They may help to learn about students' interest in different types of books, e.g., students majoring in science and engineering tend to pay more attention to computer books. Nevertheless, most of them still need to identify users' interests accurately. To solve the problem, the authors propose a novel embedding-driven model called InFo, which refers to users' intrinsic interests and academic preferences to provide personalized library book recommendations.

The authors analyze the characteristics and challenges in real library book recommendations and then propose a method considering feature interactions. Specifically, the authors leverage the attention unit to extract students' preferences for different categories of books from their borrowing history, after which we feed the unit into the Factorization Machine with other context-aware features to learn students' hybrid interests. The authors employ a convolution neural network to extract high-order correlations among feature maps which are obtained by the outer product between feature embeddings.

The authors evaluate the model by conducting experiments on a real-world dataset in one university. The results show that the model outperforms other state-of-the-art methods in terms of two metrics called Recall and NDCG.

It requires a specific data size to prevent overfitting during model training, and the proposed method may face the user/item cold-start challenge.

The embedding-driven book recommendation model could be applied in real libraries to provide valuable recommendations based on readers' preferences.

The proposed method is a practical embedding-driven model that accurately captures diverse user preferences.

Carbon management in the construction industry plays a vital role as carbon emissions have a significant impact on the environment. Current emphasis on reducing operational carbon through passive designs, zero carbon buildings and so forth has resulted in losing focus on embodied carbon (EC) reduction. Though there are various databases and tools to estimate EC in construction, these estimates are lacking in accuracy and consistency. A Blockchain-based Embodied Carbon (BEC) Estimator was developed as a solution to accurately estimate EC using a supply chain value addition concept as a methodology.

This study focused on developing, testing and validating the blockchain-based prototype system identified as BEC Estimator. The system was developed using Hyperledger Fabric following a waterfall model. Case studies and an expert forum were used to test and validate BEC Estimator.

The system architecture, development process and the user interface of BEC Estimator are presented in this paper. The comparative evaluation with existing EC databases/tools and the expert forum validated the functioning of BEC Estimator and proved it to be an accurate, secure and trustworthy EC estimating system. SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis identified the strengths and opportunities that will benefit the industry as well as the weaknesses and threats in the system that could be mitigated in future.

BEC Estimator accurately accounts for EC additions happening at each supply chain node for any product that gets incorporated in a building, thereby facilitating EC-related decision-making for all relevant stakeholders.

Performance optimization algorithms based on node attributes are of great importance for sharding blockchain systems. Currently, existing studies on blockchain sharding algorithms consider only random selection sharding strategies. However, the random selection strategy does not perfectly utilize the performance of a node to break the bottleneck of blockchain performance.

This paper proposes a blockchain sharding algorithm called TOPSIS Optimization Sharding System (TOSS), which is based on entropy weight method, relative Euclidean distance and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It defines a multi-attribute matrix to assess node performance and applies TOPSIS for scoring nodes. Then, an algorithm based on the TOPSIS method is proposed to calculate the performance score of each data node. In addition, an entropy weighting method is introduced to obtain the weights of each attribute to balance the impact of dimensional differences of attributes on the attribute weights. Nodes are ranked by composite scores to guide partitioning.

The effectiveness of the proposed algorithm in this paper is verified by comparing it with various comparative algorithms. The experimental results show that the TOSS algorithm outperforms the comparison algorithms in terms of performance improvement for the blockchain system, and the throughput metrics are improved by about 20% in comparison.

This study introduces a novel approach to blockchain sharding by incorporating the entropy weight method and relative Euclidean distance TOPSIS into the sharding process. This approach allows for a more effective utilization of node performance attributes, leading to significant improvements in system throughput and overall performance, addressing the limitations of the random selection strategy commonly used in existing algorithms.

The aim of this paper is to investigate (1) whether the different dimensions of internationalization experience have different effects on internationalization speed? (2) And how the degree of digitalization plays a moderating role in these relationships?

The authors test the hypotheses on a sample of 431 Chinese listed companies export data from 2007 to 2016, using multiple regression analysis.

The international expansion experience to developed economies will accelerate the internationalization speed of MNCs, while international expansion experience to emerging economies has an inverted U-shaped relationship with internationalization speed. The digitalization degree weakens the relationship between international experience and internationalization speed, whether it is international expansion experience to developed or emerging economies.

By decomposing the dimensions of international experience and considering the degree of digitalization as a new moderating variable, the paper helps to clarify the debate on the relationship between international experience and speed of internationalization, thus contributing to the internationalization speed literature and the digital technology perspective. Revealing the process of international experience affecting internationalization speed has implications for MNCs to achieve high-quality and rapid internationalization.

The problem of stress is increasingly gaining attention in the construction industry in recent years. This study is aimed at examining the causes, effects and possible alleviation of stress of project management (PM) practitioners so that their stress could be appropriately managed and reduced, which would contribute to improved mental health.

Primary data were collected in an online questionnaire survey via Qualtrics. Questions ranged from PM practitioners’ stressors, stress and performance under stress to stress alleviation tools and techniques. One hundred and five PM practitioners completed the questionnaire. Their responses were compiled and analyzed using descriptive statistics, correlation and regression.

The results confirmed that the identified stressors tended to increase stress of PM practitioners. All stressors tested in this study were found to have negative impact on the performance of PM practitioners. In particular, the burnout stressors were seen as the key stressors that influence the performance of PM practitioners and have a strong correlation with all the other stressors. It was also found that a number of tools and techniques can reduce the impact of stressors on PM practitioners.

This study has taken a specific focus on stress-related issues of PM practitioners in the construction industry due to their critical role in this project-dominated industry. Using the Job Demand-Resource theory, a holistic examination was not only conducted on stress and stressors but also on alleviation tools and techniques. This study has thus made significant contribution to the ongoing research aimed at finding solutions to mental health-related problems in the project-dominated construction industry, thereby achieving the United Nations’ social sustainability development goals.