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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.

The main goal of this paper is to present a synthetic multiple criteria group decision-making (MCGDM) methodology for assessing the enterprise digital maturity with linear Diophantine fuzzy (LDF) setting.

This paper utilizes the presented LDF generalized Dombi operator to aggregate assessment information of experts. The developed combined weight model through merging the rank sum (RS) model and symmetry point of criterion (SPC) method is used to ascertain the comprehensive importance of criterion. The evaluation based on distance from average solution (EDAS) approach based upon regret theory (RT) is presented to achieve the sorting of candidate enterprises.

Firstly, the proposed method has strong stability. Secondly, the proposed method takes into consideration the psychological behavior of experts during the decision-making process which further enhances the rationality of the decision results. Finally, the proposed method integrates expert and criterion weight determination models which provides a practical evaluation framework for assessing the digital maturity of enterprises. The research outcomes confirm that the proposed approach fails to resolve the decision problems with unknown weight information flexibly, but also reflect the psychological behavior of expert in decision process. The presented weight approach also provides a rational algorithm to ascertain the weight more accurate.

A composite LDF group decision-making approach is presented by aggregating the proposed generalized Dombi operator, combined weight model and the EDAS model, which make the outcome more reasonable. Sensitivity analysis and comparison study are conducted to reflect the superiority of the proposed approach.

This paper aims to investigate the actual impact, mechanism and internal and external environmental adjustment effect of ESG performance on the persistence of green innovation using data from China’s A-share manufacturing listed companies from 2009 to 2021.

Panel data regression models are used to explore the effect of ESG performance of manufacturing enterprises on the persistence of green innovation. To examine the mechanism of ESG performance affecting the persistence of green innovation of manufacturing enterprises, this paper refers to the research of Wen and Ye (2014) and constructs an analysis framework of intermediary effect.

This research was funded by Shandong Provincial Natural Science Foundation, grant number ZR2023MG075 & ZR2024QE171.

There are a few more limitations to this study that might be discussed from the following angles: first, due to data availability, this paper examines the persistence of green innovation from the output perspective. The authors can expand the data sources in the future and investigate the input-output combinations in green innovation as a means of understanding its sustainability. Second, the mechanism studied in this paper includes management costs, entry of green investors and risk-taking ability. In fact, it is possible that ESG performance influences green innovation persistence in other ways as well; these can be investigated more in the future.

First, it concentrates on the persistence of green innovation in manufacturing enterprises, surpassing the quantitative aspect and thereby broadening the research scope. Second, by including the “management expense ratio,” “green investor entry” and “risk-taking” as mediating factors, the study delves deeper into the mechanisms through which ESG performance impacts the persistence of green innovation in manufacturing enterprises, further broadening the research scope. Third, this research incorporates the internal and external environments encountered by manufacturing enterprises into the analytical framework to investigate their adjustment effects in the process of ESG performance influencing persistent green innovation, thus widening the research perspective. Fourth, this study introduces the subdimensions of ESG performance, specifically environmental responsibility, social responsibility and corporate governance, and assesses their impacts on the persistence of green innovation in manufacturing enterprises, thus enriching the research narrative.

The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph.

A correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting user semantic information, as well as introducing violent sentiment membership. To be specific, the topic of the implementation of topology mapping in the community can be obtained based on self-built field of violent sentiment dictionary (VSD) by extracting user text information. Afterward, the violence index of the user text is calculated to quantify the fuzzy sentiment representation between the user and the topic. Finally, the multi-granularity violence association rules mining of user text is realized by constructing violence fuzzy concept lattice.

It is helpful to reveal the internal relationship of online violence under complex network environment. In that case, the sentiment dependence of users can be characterized from a granular perspective.

The membership degree of violent sentiment into user relationship recognition in Fancircle community is introduced, and a text sentiment association recognition method based on VSD is proposed. By calculating the value of violent sentiment in the user text, the annotation of violent sentiment in the topic dimension of the text is achieved, and the partial order relation between fuzzy concepts of violence under the effective confidence threshold is utilized to obtain the association relation.

This paper aims to identify a suitable convolutional neural network (CNN) model to analyse where void(s) are formed in asymmetrical flip-chips with large amounts of the ball-grid array (BGA) during underfilling.

A set of void(s)-filled through-scan acoustic microscope (TSAM) images of BGA underfill is collected, labelled and used to train two CNN models (You Look Only Once version 5 (YOLOv5) and Mask RCNN). Otsu's thresholding method is used to calculate the void percentage, and the model's performance in generating the results with its accuracy relative to real-scale images is evaluated.

All discoveries were authenticated concerning previous studies on CNN model development to encapsulate the shape of the void detected combined with calculating the percentage. The Mask RCNN is the most suitable model to perform the image segmentation analysis, and it closely matches the void presence in the TSAM image samples up to an accuracy of 94.25% of the entire void region. The model's overall accuracy of RCNN is 96.40%, and it can display the void percentage by 2.65 s on average, faster than the manual checking process by 96.50%.

The study enabled manufacturers to produce a feasible, automated means to improve their flip-chip underfilling production quality control. Leveraging an optimised CNN model enables an expedited manufacturing process that will reduce lead costs.

BGA void formation in a flip-chip underfilling process can be captured quantitatively with advanced image segmentation.

The mega railway infrastructure projects are faced with complex environments and multi-level management challenges. Thus, the mega railway infrastructure project management system not only needs to focus on its composition, but also needs to consider changes and impacts of internal and external environment.

This study attempts to introduce the concept of dissipative structure from the perspective of complexity theory and constructs a positive entropy and negentropy flow index system for mega railway infrastructure project management system in order to analyze the factors of management system more deeply. The Brusselator model is used to construct the structure of the mega railway infrastructure project management system, and the entropy method is used to calculate the positive entropy and negentropy values to verify whether the management system is a dissipative structure.

A plateau railway project in China was used as an example for an empirical study, not only its own characteristics are analyzed, but also the role of constraints and facilitation of the internal and external environment. Based on the research results, several effective suggestions are put forward to improve the stability and work efficiency of mega railway infrastructure project management system.

This study demonstrates that mega railway infrastructure project management system has the characteristics of dissipative structure. It can provide theoretical support for the development of mega railway infrastructure project management system from disorderly state to orderly state.

This study examines whether common directors are guilty of contagion of corporate frauds from the customer side and, if so, how contagion occurs. Moreover, it explores a way to mitigate it, which is the increased digital orientation of firms.

Secondary data analysis is applied in this paper. We extract supply chain relations from the China Stock Market and Account Research (CSMAR) database as well as corporate fraud data from the same database and the official website of the China Securities Regulatory Commission (CSRC). Digital orientations are estimated through text analysis. Poisson regression is conducted to examine the moderating effect of common directors and the moderated moderating effect of the firms’ digital orientations.

By analysing the 2,096 downstream relations from 2000 to 2021 in China, the study reveals that corporate frauds are contagious through supply chains, while only customers’ misconduct can contagion to upstream firms. The presence of common directors strengthens such supply chain contagion. Additionally, the digital orientation can mitigate the positive moderating effect of common directors on supply chain contagion.

This study highlights the importance of understanding supply chain contagion through corporate fraud by (1) emphasising the existence of the contagion effects of corporate frauds; (2) understanding the potential channel in the process of contagion; (3) considering how digital orientation can mitigate this contagion and (4) recognising that the effect of contagion comes only from the downstream, not from the upstream.

Although businesses increasingly use augmented reality (AR) to enhance service experiences, the way AR service marketing inspires consumers remains underexplored. Drawing on the consumer inspiration literature, the authors examine how AR service marketing activities such as entertainment, interaction, trendiness and customization enhance consumer inspiration. In addition, the authors explore the role of consumer empowerment and skepticism as key underlying mechanisms between consumer inspiration and value co-creation (VCC) or co-destruction (VCD) intentions.

The study used a mixed method, explanatory sequential design to gain a more comprehensive understanding of their proposed theoretical framework. The quantitative survey study involved 344 AR app users, followed by a qualitative open-ended essay study with 34 AR app users.

Results suggest that AR service marketing activities positively influence consumer inspiration, which in turn increases consumer empowerment and reduces skepticism. The authors also found that consumer empowerment leads to VCC, while skepticism leads to VCD. These findings provide valuable insights for practitioners seeking to implement AR service marketing activities effectively to inspire consumers, foster value creation and manage value destruction.

The study highlights inspiration as a key factor in motivating consumers to co-create value, transcending typical service experiences and limitations. Empowered consumers, feeling inspired, are more inclined to contribute effectively to VCC, also fostering trust in the service provider. AR serves not just as a sales channel, but also as a tool for relationship-building and brand retention. Managers should leverage AR to elicit feelings of trendiness, customization and interaction, fostering empowerment and inspiring consumers to co-create value.

This study significantly contributes to the growing body of literature on consumer inspiration and AR service marketing. It emphasizes the need to consider external (i.e. marketing-induced) stimuli in understanding the sources and consequences of consumer inspiration through AR.

A popular production model for high-tech manufacturers is that they move most production lines abroad to produce formal products for sale and just keep a few production lines in headquarters to manufacture sample products for new product development. Under such a production model, the paper aims to develop a selection model of International Air Express (IAE) for high-tech manufacturers in airfreight of sample products using the fuzzy best-worst method (BWM).

In this paper, an assessment model based on the fuzzy BWM approach is proposed for high-tech manufacturers in selecting airfreight carriers for the shipping of sample products. Further, one high-tech electronic manufacturer in Taiwan was empirically investigated to validate the assessment model.

The result indicates that electronics manufacturer pays more attention to Promptness, Mutual trust, Freight rate and Financial status of fixed assets when selecting IAEs. Besides, FedEx is argued to be the most preferred IAE for the transportation of sample products. Based on the findings, some practical management implications were discussed.

Some literature limitations should be addressed. Initially, the adoption of the fuzzy BWM assumes independence among criteria. Nonetheless, this assumption is not yet to confirm in this study. Accordingly, this limitation leaves room for improvement in future studies. Further, in this paper, five experienced experts from the Radiant Opto-Electronics Corporation (ROEC) case were empirically surveyed. To ensure the validity of the surveying, this paper adopted an interviewing survey instead of a traditional mailed survey. However, more representative samples are still necessary to confirm the empirical results in future research.

Firstly, the proposed research model provides a systematic framework to the decision-making process, which assists high-tech manufacturers in identifying the most suitable IAEs based on multiple criteria. It has been illustrated that high-tech companies deliver their sample products requiring timely and secure means of transport. In practice, manufacturers can assess various IAEs considering some main factors, such as Operational Flexibility (OF), Partner Relationship (PR), Transportation Capability (TC) and Management, using fuzzy BWM. This process ensures the selection of IAEs aligning with their logistical needs and business priorities, ultimately enhancing operational efficiency and customer satisfaction. Secondly, empirical results from the ROEC case indicate that electronics manufacturer pays more attention to Promptness, Mutual trust, Freight rate and Financial status of fixed assets when selecting IAEs. Besides, FedEx is argued to be the most preferred IAE for transportation of sample products. In other words, ROEC should consider establishing long-term contracts with preferred IAEs (i.e. FedEx) to secure favorable rates and service commitments. On top of that, results not only provide practical information for manufacturers in selecting IAEs but also for IAE partners to improve their service policies.

The results not only provide practical information for high-tech manufacturers in selecting airfreight carriers but also for the airfreight carriers to improve their service quality.

In contrast to other Chinese sports, which attract enormous commercial interests and relates to a kind of political kudos and ideological significance, Chinese baseball represents a singular case for the author to investigate the way in which China responds to sport globalisation due to its peripheral position in, and limited resource from, the domestic sport system and powerful stakeholders from the global society. The study examines the development of Chinese baseball and its interplay with the global sport giants and international events (i.e. the Olympic Games and the Major League Baseball [MLB]). Given the influence of sport globalisation, the author also identifies the consistent dominance of Chinese sport authorities and the spectrum of local stakeholders' reaction towards globalisation. The chapter ends with a discussion of the relationship between marginal position of Chinese baseball in the local sport system and the rationale of its response to sport globalisation.