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The aim was to investigate the effect of normal load on the tribological performance of laser cladded FeCoCrMoSi amorphous coating, which might choose the appropriate normal load for the friction reduction and wear resistance.

A FeCoCrMoSi amorphous coating was prepared on 45 steel using laser cladding, and the tribological performance of obtained coating under the different normal loads was investigated using a ball-on-disk tribometer.

The FeCoCrMoSi amorphous coating is composed of M₂₃C₆, Co₆Mo₆C₂ and amorphous phases, where the M₂₃C₆ hard phase enhances the coating hardness to increase the wear resistance and the Co₆Mo₆C₂ with the vein shape forms the strong mechanical interlock to play the role of friction reduction. The average coefficients of friction of containing amorphous FeCoCrMoSi coating under the normal loads of 3, 4 and 5 N are 0.68, 0.65 and 0.53, respectively, and the corresponding wear rates are 17.7, 23.9 and 21.9 µm3•N⁻¹•mm⁻¹, respectively, showing that the appropriate normal load is beneficial for improving its friction reduction and wear resistance. The wear mechanism is composed of adhesive wear, abrasive wear and oxidative wear, which is attributed to the high hardness of amorphous coating by the amorphous phase.

The FeCoCrMoSi amorphous coating was first applied for the improvement of 45 steel, and the effect of normal load on its tribological performance was investigated.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0304/

This paper aims to investigate the relationship between empowering leadership and innovative job performance, with challenge stress and hindrance stress acting as parallel mediators. Additionally, the study examines how promotion focus and prevention focus moderate these dual processes.

A two-wave survey was employed to validate the theoretical model, gathering data from 449 employees across various industries in Mainland China with a convenience sampling method.

The results demonstrate that empowering leadership enhances employee innovative job performance by increasing challenge stress and reducing hindrance stress, highlighting the moderating role of regulatory focus. Specifically, a high promotion focus strengthens the positive relationship between empowering leadership and challenge stress, while a high prevention focus weakens the negative relationship between empowering leadership and hindrance stress. The moderated mediation effect of regulatory focus is also verified.

Empowering leaders should be mindful of employees’ dualistic work stress and implement tailored management strategies based on individual regulatory focus to maintain their psychological well-being and enhance innovative performance.

Grounded in job demand-resource (JD-R) theory and a stress perspective, this study develops a dual-path model to explore the impact of empowering leadership on employee innovative job performance through dualistic work stress. This framework enhances our understanding of the mechanisms underlying the effectiveness of empowering leadership and the antecedent factors influencing employee well-being and innovative performance. Furthermore, by examining the role of employees’ regulatory focus, this study clarifies the boundary conditions of empowering leadership effectiveness, addressing inconsistencies in previous research findings.

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.

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.

To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.