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New generation of construction workers (NGCWs) who were born in the 1980s and later have gradually become the main workforce of Chinese construction industry. They may behave differently when dealing with knowledge-related activities due to divergent characteristics caused by generational discrepancy. To provide a theoretical foundation for construction companies and safety managers to improve safety management, this research explores the factors and paths impacting the NGCWs' ability to share their safety knowledge.

Based on literature review, main factors that influence the safety knowledge sharing of the NGCWs were identified. Decision-Making Trial and Evaluation Laboratory and Interpretive Structural Modeling were applied to identify the hierarchical and contextual relations among the factors influencing the safety knowledge sharing of the NGCWs.

The results showed that sharing atmosphere ranked first in centrality and had a high degree of influence and being influenced, indicating itself an extremely important influencing factor of safety knowledge sharing of NGCWs. Six root influencing factors were identified, including individual characteristics, work pressure, sharing platform, incentive mechanism, leadership support and safety management system.

The number of influencing factors of safety knowledge sharing of the NGCWs identified in this study is limited, and the data obtained by the expert scoring method is subjective. In future studies, the model should be further developed and validated by incorporating experts from different fields to improve its integrity and applicability.

The influencing factors identified in this paper can provide a basis for construction companies and safety managers to improve productivity and safety management by taking relevant measures to promote safety knowledge sharing. The research contributes to the understanding knowledge management in the context of the emerging market. It helps to answer the question of how the market can maintain the economic growth success through effective knowledge management.

This paper investigates the influencing factors of NGCWs' safety knowledge sharing from the perspective of intergenerational differences, and the 13 influencing factor index system established expands the scope of research on factors influencing safety knowledge sharing among construction workers and fills the gap in safety knowledge sharing research on young construction workers. Furthermore, this paper establishes a multi-layer recursive structure model to clarify the influence path of the influencing factors and contributes to the understanding of safety knowledge sharing mechanism.

The demand for the cold chain logistics of agricultural products was investigated through demand forecasting; targeted suggestions and countermeasures are provided. This paper aims to discuss the aforementioned statement.

A Markov-optimised mean GM (1, 1) model is proposed to forecast the demand for the cold chain logistics of agricultural products. The mean GM (1, 1) model was used to forecast the demand trend, and the Markov chain model was used for optimisation. Considering Guangxi province as an example, the feasibility and effectiveness of the proposed method were verified, and relevant suggestions are made.

Compared with other models, the Markov-optimised mean GM (1, 1) model can more effectively forecast the demand for the cold chain logistics of agricultural products, is closer to the actual value and has better accuracy and minor error. It shows that the demand forecast can provide specific suggestions and theoretical support for the development of cold chain logistics.

This study evaluated the development trend of the cold chain logistics of agricultural products based on the research horizon of demand forecasting for cold chain logistics. A Markov-optimised mean GM (1, 1) model is proposed to overcome the problem of poor prediction for series with considerable fluctuation in the modelling process, and improve the prediction accuracy. It finds a breakthrough to promote the development of cold chain logistics through empirical analysis, and give relevant suggestions based on the obtained results.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

The purpose of this paper is to develop an equation for the synergistic corrosion of SRB and CO₂ based on the D-W model.

The bacterial types in the a and ß pipelines were studied by the most probable number method, and the corrosion morphology of L360 in pipeline water samples was studied by surface analysis. The corrosion rate of L360 was studied using the weight loss method. The gray correlation method was used to calculate the degree of correlation between the influencing factors of corrosion under the synergistic effect of CO₂ and SRB. The curve obtained from PIPESIM software and experiments data was then fitted using multiple non-linear regression method by MATLAB software.

The equation was used to predict the corrosion of the ß pipeline for verification, and it was found that seven out of ten excavation sites were within a 20% error range.

Using the gray correlation method, an equation that considers synergistic corrosion of SRB and CO₂ has been developed based on the D-W model. The equation could be used to predict the corrosion rate of shale gas gathering pipelines through SRB and CO₂ synergistic corrosion.