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Conflicts frequently occur in construction projects. Matching appropriate contractual and relational governance with conflict features to promote cooperation and thus minimize the negative influences of conflict is an issue that deserves attention. Our study classifies conflict types into task, process and relationship conflict and defines their combinations as conflict profiles. By conceptualizing contractual governance as the complexity of contract provisions and the strictness of contract enforcement and relational governance as trust, our study aims to explore the configurational impacts of conflict profiles and these governance mechanisms on parties’ cooperative behaviors.

A questionnaire survey was conducted, and 238 valid questionnaires were received. Fuzzy set qualitative comparative analysis was performed.

Four configurations produce cooperative behaviors. The combined use of detailed contracts, rigid enforcement and high trust enhances cooperation and such a governance arrangement is not subject to any conflict profile. A relatively low level of conflict requires detailed contracts and high levels of trust. For the conflict profile characterized by high task and process conflict and low relationship conflict, parties can select contractual governance-dominant or relational governance-dominant approaches.

Theoretically, our study reveals the matching relationships between conflict profiles and governance mechanisms, enriching the research on conflict profiles in construction projects and the interrelation between contractual and relational governance. Practically, the findings provide project managers guidance for conflict management and selecting governance mechanisms.

This paper aims to find a suitable solution to degrade the C.I. Reactive Red 24 (RR24) dyeing wastewater by using sodium persulphate to recycle water and inorganic salts.

The effects of temperature, the concentration of inorganic salts and Na₂CO₃ and the initial pH value on the degradation of RR24 were studied. Furthermore, the relationship between free radicals and RR24 degradation effect was investigated. Microscopic routes and mechanisms of dye degradation were further confirmed by testing the degradation karyoplasmic ratio of the product. The feasibility of the one-bath cyclic dyeing in the recycled dyeing wastewater was confirmed through the properties of dye utilization and color parameters.

The appropriate conditions were 0.3 g/L of sodium persulphate and treatment at 95°C for 30 min, which resulted in a decolorization rate of 98.4% for the dyeing wastewater. Acidic conditions are conducive to rapid degradation of dyes, while ·OH or SO₄⁻· have a destructive effect on dyes under alkaline conditions. In the early stage of degradation, ·OH played a major role in the degradation of dyes. For sustainable cyclic dyeing of RR24, inorganic salts were reused in this dyeing process and dye uptake increased with the times of cycles. After the fixation, some Na₂CO₃ may be converted to other salts, thereby increasing the dye uptake in subsequent cyclic staining. However, it has little impact on the dye exhaustion rate and color parameters of dyed fabrics.

The recommended technology not only reduces the quantity of dyeing wastewater but also enables the recycling of inorganic salts and water, which meets the requirements of sustainable development and clean production.

Peer-to-peer lending has emerged as a promising alternative investment avenue globally. This study explores the variables influencing investors' intention in peer-to-peer lending. Studying these factors is crucial for inspiring investor motivation, which leads to the growth of peer-to-peer lending platforms in India.

The research utilizes information gathered from 293 investors of the Rang de platform through a questionnaire. It employs ordinal logistic regression to assess how demographic characteristics, investor experience, social projects and investment factors influence the study’s outcomes.

The research indicates that the education qualification and income level of the investors are significant demographic variables in their investment decisions. Moreover, social projects, the experience of the investors and investment factors affect their intent to invest more in peer-to-peer lending projects.

It helps stakeholders and borrowers of crowdfunding platforms think about these aspects when creating plans to bring in and keep investors. This approach also enhances the openness and dependability of lending processes.

This research sheds light on Indian investors' behavior toward peer-to-peer lending platforms and includes new investment factors like project categories and impact partners that other researchers did not identify.

Under the “dual carbon” framework, the article explores the equilibrium points among the government, agricultural enterprises and village committees, and uses sensitivity analysis to reveal the dynamic factors affecting these stakeholders, thereby proposing methods to enhance agricultural disaster resilience.

The article uses MATLAB to construct a game model for the three parties with interests: agribusiness, government and village council. It examines the stability of strategies among these entities. Through graphical simulation, the paper analyzes the sensitivity of agricultural enterprises carbon emissions and village committees’ rent-seeking behaviors in the decision-making process, focusing on significant factors such as government carbon tax and regulatory policies.

A single government reward and punishment mechanism is insufficient to influence the strategic choices of enterprises and village committees. The cost of rent-seeking does not affect the strategic choices of enterprises and village committees. A key factor influencing whether the village committee engages in rent-seeking is the level of labor income of the village committee as an “intermediary”.

This paper focuses on the dynamic game between three stakeholders (the government, agricultural enterprises and village committees), seeking dynamic equilibrium and conducting sensitivity analysis through visualization to provide the government with optimal policy recommendations.

The purpose of this study is to enhance the safety and comfort of tourists in scenic areas undergoing renovation and transformation by developing a comprehensive safety assessment model that takes into account both internal and external factors affecting tourist and construction safety.

The research employs a multi-level tourist-construction interaction safety assessment index system, which is constructed through a deep analysis of factors such as the construction environment, tourist behavior and safety signs. The study utilizes game theory in conjunction with three main objective and subjective weight distribution methods to determine the weights of the index system, ensuring the objectivity and effectiveness of the assessment results. The cloud model and cloud generator are applied for the language transformation of the indicators, leading to a comprehensive assessment of construction safety.

The survey results indicate that the safety risks of the case project are relatively high, with limited impact of time segments on safety risks, and the risk level during weekends is slightly higher than on weekdays, but the difference is not significant. Among the reviewed influencing factors, compliance with safety signs and the proportion of people crossing construction areas are the factors with the highest risk level, representing a large number of tourists ignoring safety guidance and forcibly crossing construction areas, facing construction dangers, posing a great challenge to safety management.

This study offers a novel methodological approach to safety risk assessment in similar environments, contributing to the field by improving the systematicness and scientific nature of safety management. It provides a scientific assessment tool for the safety management of tourists in scenic area renovation projects, aiming to achieve the dual objectives of tourist safety and construction efficiency.

The Grey System Theory (GST) is an emerging area of research within artificial intelligence. Since its founding in 1982, it has seen a lot of multidisciplinary applications. In just a short period, it has garnered some considerable strengths. Based on the 1987–2021 data collected from the Web of Science (WoS), the current study reports the advancement of the GST.

Research papers utilizing the GST in the fields of economics and education were retrieved from the Web of Science (WoS) platform using a set of predetermined keywords. In the final stage of the process, the papers that underwent analysis were manually chosen, with selection criteria based on the information presented in the titles and abstracts.

The study identifies prominent authors, institutions, publications and journals closely associated with the subject. In terms of authors, two major clusters are identified around Liu SF and Wang ZX, while the institution with the highest number of publications is Nanjing University of Aeronautics and Astronautics. Moreover, significant keywords, trends and research directions have been extracted and analyzed. Additionally, the study highlights the regions where the theory holds substantial influence.

The study is subject to certain limitations stemming from factors such as the language employed in the chosen literature, the papers included within the Web of Science (WoS) database, the designation of works categorized as “articles” in the database, the specific selection of keywords and keyword combinations, and the meticulous manual process employed for paper selection. While the manual selection process itself is not inherently limiting, it demands a greater investment of time and meticulous attention, contributing to the overall limitations of the study.

The significance of the study extends not only to scholars and practitioners but also to readers who observe the development of emerging scientific disciplines.

The analysis of trends revealed a growing emphasis on the application of GST in diverse domains, including supply chain management, manufacturing and economic development. Notably, the emergence of COVID-19 as a new research focal point among GST scholars is evident. The heightened interest in COVID-19 can be attributed to its global impact across various academic disciplines. However, it is improbable that this interest will persist in the long term, as the pandemic is gradually brought under control.

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.