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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.

In China, it is prevalent for parents to share health information on WeChat and receive feedback from their children. This study aims to investigate the feedback from younger generations regarding their parents’ health information sharing. It will examine the different types of feedback, explore the factors influencing it and analyze the outcomes that result from this feedback exchange.

The empirical findings draw on the qualitative analysis using grounded theory. This study collects data from 34 participants (17 pairs of one young person and one parent) through in-depth interviews and WeChat chat records. Then, a theoretical model was developed through open, axial and selective coding.

Feedback can be classified into five types: support, correction, perfunctoriness, ostracism and rejection as well as into “Affective-Behavioral-Cognitive” dimensions. Younger generations’ feedback types are influenced by a variety of factors, including information, emotion and individual and family-related factors. Each feedback type has distinct effects, such as altering older generations’ emotional and communication responses.

This pioneering study explores how younger generations in China perceive their parents’ health information sharing on social media. It highlights the importance of feedback in this context, providing actionable insights to enhance digital literacy among older adults, strengthen family bonds and enhance the spread of valuable and scientific health information online.

In response to the complex external structure of high-precision aviation plugs, which makes it difficult to search outside the hole and adjust inside the hole during automated assembly. This paper aims to propose an assembly framework that combines multi-agent search and variable parameter compliant control to solve this problem.

First, a multi-agent search strategy (MAS) based on Gaussian Mixture Model and Deep Q-Network was proposed to optimize displacement direction and actions, thereby improving search speed and success rate. Then, a variable parameter admittance control method (RL-VPA) based on dual delay depth deterministic policy gradient (TD3) was proposed, which dynamically optimized the internal parameters of the admittance controller and adopted state space discretization to improve convergence speed and assembly efficiency.

Compared to spiral search and single-agent search, the average search success rate has improved by approximately 10% and 6.6%. Compared to fixed admittance control and other RL-based methods, the average assembly success rate has increased by approximately 38.6%, 22% and 8.6%. Compared with the training results of the model without state discretization, it was found that state discretization helps the model converge quickly. To verify the generalization ability of the assembly framework, experiments were conducted on three different pin counts of aviation plugs, the assembly success rate reached 86.7%, all of which showed good assembly results. Finally, combining state space discretization to reduce the impact of environmental noise, improve training effectiveness and convergence speed.

MAS has been proposed to optimize displacement direction and action, improving search speed and success rate. RL-VPA is designed to dynamically optimize the internal parameters of the admittance controller, enhancing the robustness and generalization ability of the model. Additionally, state space discretization is combined to improve training effectiveness and convergence speed.

This study aims to improve the stiffness of as-printed handles by finding appropriate printing orientations.

First, a series of benchmark handles is designed using Taguchi method. Then, for each uniformly sampled printing orientation, every benchmark handle is sliced and undergoes stiffness evaluation (i.e. displacement and mean stress) by using finite element analysis (FEA). This generates a substantial batch of handle-orientation-stiffness samples. With the data, an effective stiffness-prediction network is developed based on the artificial neural network. Finally, using the developed network, the particle swarm optimization is adapted to determine the optimized printing orientation for each input handle, aiming to improve its stiffness.

Compared with the common slicing software, the printing orientations proposed in this study, based on FEA, result in varying degrees of improvement in stiffness for four handles. Specifically, the displacement and mean stress are reduced by 16.86% and 18.14% on average. The experiments show that the approach has the potential to effectively improve the stiffness of a handle.

Although the anisotropic property in mechanics is unavoidable and difficult to formally describe in 3D printing, the proposed approach can effectively characterize the relationship between the stiffness and the printing orientation for each handle. And, it also can determine an optimized printing orientation for each handle to enhance its stiffness after printing.

Megaprojects provide an ideal context for exploring the dynamic characteristics of stakeholders within a collaborative innovation system. This research aims to examine the changes in stakeholder salience and functional roles during the evolution of such a system.

This study is empirically grounded on the Hong Kong-Zhuhai-Macao Bridge (HZMB) megaproject in China, analyzed with the stakeholder salience, stakeholder functional role, stakeholder dynamics and collaborative innovation system theoretical lens.

The megaproject collaborative innovation system can be divided into four stages: the birth stage, development stage, mature stage and re-innovation stage. Stakeholder salience generally remains unchanged throughout the lifecycle of the collaborative innovation system, except for engineering consulting firms (ECF). ECF transitioned from a definitive stakeholder to an expectant stakeholder upon project completion. The number of definitive stakeholders during the first three stages increases gradually. Besides, stakeholder functional roles shift in eight different directions throughout the lifecycle of the megaproject collaborative innovation system because they possess different core resources necessary for implementing innovations and are positioned differently within the collaborative innovation system.

This study contributes to the theory and practice of collaborative innovation in megaprojects. First, it offers insights into the evolution of megaproject collaborative innovation systems from the perspective of stakeholder interactions. Second, it has significant implications for managing stakeholder relationships based on their salience and functional roles at different stages of the collaborative innovation system.

With the rapid development of the pipeline transportation and exploitation of mineral resources, it is urgent requirement for the high-performance polymer matrix composites with low friction and wear to meet the needs of solid material transportation. This paper aims to prepare high-performance ultrahigh molecular weight polyethylene (UHMWPE) matrix composites and investigate the effect of service condition on frictional behavior for composite.

In this study, UHMWPE matrix composites with different content of MoS₂ were prepared and the tribological performance of the GCr15/composites friction pair in various sliding speeds (0.025–0.125 m/s) under dry friction conditions were studied by ball-on-disk tribology experiments.

Results show that the frictional behavior was shown to be sensitive to MoS2 concentration and sliding velocity. As the MoS2 content is 2 Wt.%, composites presented the best overall tribological performance. Besides, the friction coefficient fluctuates around 0.21 from 0.025 to 0.125 m/s sliding speed, while the wear rate increases gradually. Scanning electron microscopy images, energy-dispersive spectroscopy and Raman Spectrum analysis present that the main wear mechanisms were abrasive and fatigue wear.

The knowledge obtained herein will facilitate the design of UHMWPE matrix composites with promising self-lubrication performances which used in slag transport engineering field.

In the presence of coronavirus disease 2019 (COVID-19), due to the social distance restriction, consumers' regular consumption behaviors and patterns have been changing fundamentally. Thereafter, an innovative group buying model has emerged and developed explosively with a specific focus on consumer's location, known as community-based group buying (CGB). The purpose of this paper is to investigate the transfer mechanism of user's trust in dyadic contexts of social and commercial role-playing in the CGB program.

This study adopts an empirical research method, with an online and offline questionnaire survey, a total of 382 responses have been obtained. Then, both descriptive analysis and hierarchical regression analysis are conducted to explore the dual roles of group leader and its corresponding effects on consumers' trust (i.e. emotional trust and behavioral trust) and engagement actions (i.e. purchase and share) in the CGB program.

Results indicate that resident's trust and their perception of group leader's friend role can positively enhance their engagement actions in the CGB programs. Meanwhile, for the purpose of profit maximization, the group leader is more willing to play a friend role in transactions no matter whether the role conflict exists.

Research findings provide some managerial insights for CGB platform on the selection and training of group leaders and the incentive mechanism design.

Financial technology (FinTech) has enhanced the inclusivity and accessibility of traditional finance, offering a novel pathway for rural revitalization and development. The paper aims to assess the rural revitalization development level in prefecture-level cities in China and investigate the potential impact mechanism of FinTech.

This paper develops an index system to evaluate the rural revitalization level across 279 cities in China from 2011 to 2021. In addition, multi-mediation and threshold models are employed to analyze how FinTech influences rural revitalization.

The results reveal that, first, FinTech has significantly promoted rural revitalization. Second, entrepreneurial activeness, innovation capability, and industrial structure advancement are intermediary factors within the benchmark path. Third, FinTech exhibits varied threshold effects in entrepreneurial activeness, innovation capability, and industrial structure advancement, influencing rural revitalization with diverse impacts.

First, this paper expands the rural revitalization evaluation to include 30 indexes, enhancing overall measurement comprehensiveness. Second, in contrast to previous research concentrating on provincial-level assessments, this paper explores rural revitalization across 279 cities in China from 2011 to 2021, broadening the study’s scope and timeline. Third, this paper delves into empirical evidence illustrating how FinTech contributes to rural revitalization through entrepreneurial activeness, urban innovation capability, and industrial structure advancement, thereby deepening research in this domain.

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 purpose of this study is to provide a holistic understanding of the factors that either promote or hinder the adoption of artificial intelligence (AI) in supply chain management (SCM) and operations management (OM). By segmenting the AI lifecycle and examining the interactions between critical success factors and critical failure factors, this study aims to offer predictive insights that can help in proactively managing these factors, ultimately reducing the risk of failure, and facilitating a smoother transition into AI-enabled SCM and OM.

This study develops a knowledge graph model of the AI lifecycle, divided into pre-development, deployment and post-development stages. The methodology combines a comprehensive literature review for ontology extraction and expert surveys to establish relationships among ontologies. Using exploratory factor analysis, composite reliability and average variance extracted ensures the validity of constructed dimensions. Pearson correlation analysis is applied to quantify the strength and significance of relationships between entities, providing metrics for labeling the edges in the resource description framework.

This study identifies 11 dimensions critical for AI integration in SCM and OM: (1) setting clear goals and standards; (2) ensuring accountable AI with leadership-driven strategies; (3) activating leadership to bridge expertise gaps; (4) gaining a competitive edge through expert partnerships and advanced IT infrastructure; (5) improving data quality through customer demand; (6) overcoming AI resistance via awareness of benefits; (7) linking domain knowledge to infrastructure robustness; (8) enhancing stakeholder engagement through effective communication; (9) strengthening AI robustness and change management via training and governance; (10) using key performance indicators-driven reviews for AI performance management; (11) ensuring AI accountability and copyright integrity through governance.

This study enhances decision-making by developing a knowledge graph model that segments the AI lifecycle into pre-development, deployment and post-development stages, introducing a novel approach in SCM and OM research. By incorporating a predictive element that uses knowledge graphs to anticipate outcomes from interactions between ontologies. These insights assist practitioners in making informed decisions about AI use, improving the overall quality of decisions in managing AI integration and ensuring a smoother transition into AI-enabled SCM and OM.