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The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

Complex cost structures and multiple conflicting objectives make selecting an appropriate cloud service difficult. The purpose of this study is to propose a novel group consensus decision making method for cloud services selection with knowledge deficit by trust functions.

This article proposes a knowledge deficit-based multi-criteria group decision-making (MCGDM) method for cloud-service selection based on trust functions. Firstly, the concept of trust functions and a ranking method is developed to express the decision-making opinions. Secondly, a novel 3D normalized trust degree (NTD) is defined to measure the consensus levels. Thirdly, a knowledge deficit-based interactive consensus model is proposed for the inconsistent experts to modify their decision opinions. Finally, a real case study has been carried out to illustrate the framework and compare it with other methods.

The proposed method is practical and effective which is verified by the real case study. Knowledge deficit is an important concept in cloud service selection which is verified by the comparison of the proposed recommended mechanism based on KDD with the conventional recommended mechanism based on average value. A 3D NTD which considers three values (trust, not trust and knowledge deficit) is defined to measure the consensus levels. A knowledge deficit-based interactive consensus model is proposed to help decision-makers reach group consensus. The proposed group consensus model enables the inconsistent decision-makers to accept the revised opinions of those with less knowledge deficit, rather than accepting the recommended opinions averagely.

The proposed a knowledge deficit-based MCGDM cloud service selection method considers group consensus in cloud service selection. The concept of knowledge deficit is considered in modeling the group consensus measuring and reaching method.

The purpose of this paper is to propose novel attitudinal prioritization and correlated aggregating methods for multiple attribute group decision making (MAGDM) with triangular intuitionistic fuzzy Choquet integral.

Based on the continuous ordered weighted average (COWA) operator, the triangular fuzzy COWA (TF-COWA) operator is defined, and then a novel attitudinal expected score function for triangular intuitionistic fuzzy numbers (TIFNs) is investigated. The novelty of this function is that it allows the prioritization of TIFNs by taking account of the expert’s attitudinal character. When the ranking order of TIFNs is determined, the triangular intuitionistic fuzzy correlated geometric (TIFCG) operator and the induced TIFCG (I-TIFCG) operator are developed.

Their use is twofold: first, the TIFCG operator is used to aggregate the correlative attribute value; and second, the I-TIFCG operator is designed to aggregate the preferences of experts with some degree of inter-dependent. Then, a TIFCG and I-TIFCG operators-based approach is presented for correlative MAGDM problems. Finally, the propose method is applied to select investment projects.

Based on the TIFCG and I-TIFCG operators, this paper proposes a novel correlated aggregating methods for MAGDM with triangular intuitionistic fuzzy Choquet integral. This method helps to solve the correlated attribute (criteria) relationship. Furthermore, by the attitudinal expected score functions of TIFNs, the propose method can reflect decision maker’s risk attitude in the final decision result.

The elderly service industry is emerging in China. The Chinese government introduced a series of policies to guide elderly service enterprises to improve their service quality. This study explores novel differentiated subsidy strategies that not only promote the improvement of service quality in elderly service enterprises but also alleviate the financial burden on the government.

Evolutionary game and Hotelling models are employed to investigate this issue. First, a Hotelling model that considers consumer word-of-mouth preferences is established. Subsequently, an evolutionary game model between local governments and enterprises is constructed, and the evolutionary stable strategies of both parties are analyzed. Finally, simulation experiments are conducted.

The findings indicate that local government decisions have a significant influence on the behavior of elderly service enterprises. Increasing the proportion of local governments opting for subsidy strategies helps incentivize elderly service enterprises to improve their service quality. Furthermore, providing differentiated subsidies based on the preferences of the customer base of elderly service enterprises can encourage service quality improvement while reducing government expenditure. The findings offer valuable insights into the design of government subsidy policies.

Compared with previous research, this study examines the role of consumer preferences in a differentiated subsidy policy. This enriches the authors’ understanding of the field by incorporating neglected aspects of consumer preferences in the context of the emerging elderly service industry.

This study aims to examine the relationship between financial development (FD), financial risk, green finance and innovation related to carbon emissions in the G7 economies.

This quantitative study examines the roles that financial development [FD: Domestic credit to private sector by banks as percentage of gross domestic product (GDP)], economic growth (GDP: Constant US$ 2015), financial risk index (FRI), green finance (GFIN: Renewable energy public research development and demonstration (RD&D) budget as percentage of total RD&D budget), development of environment-related technologies (DERTI: percentage of all technologies) and human capital (HCI: index) have on the environmental quality of developed economies. Based on panel data, the study uses a novel approach method of moments quantile regression as a main method to tackle the issue of cross-sectional dependency, slope heterogeneity and nonnormality of the data.

The study confirms that increasing economic development increases emissions and negatively impacts the environment. However, efficient resource allocation, improved financial systems, and green innovation are likely to contribute to emission mitigation and the overall development of a sustainable viable economy. Furthermore, the study highlights the importance of risk management in financial systems for future emissions prevention.

The study uses a reliable estimation procedure, which extends the discussion on climate policy from a COP-27 perspective and offers practical implications for policymakers in developing more effective emission mitigation strategies.

The study offers policy suggestions for a sustainable economy, focusing on both COP-27 and the G7 countries. Recommendations include implementing carbon pricing, developing carbon capture and storage technologies, investing in renewables and energy efficiency and introducing financial instruments for emission mitigation. From a COP-27 standpoint, the G7 should prioritize transitioning to low-carbon economies and supporting developing nations in their sustainability efforts to address the pressing challenges of climate change and global warming.

In comparison to the literature, this study examines the importance of financial risk for G7 economies in promoting a sustainable environment. More specifically, in the context of FD and national income with carbon emissions, previous researchers have disregarded the importance of green innovation and human capital, so the current study fills the gap in the literature related to G7 economies by exploring the link between the identified variables related to carbon emissions.

Blockchain technology has been recognized as a potential solution to the challenges in managing healthcare information. Its adoption in the healthcare industry has garnered the attention of healthcare institutions and governments. Given the significant role of subsidies in promoting technology adoption, this study applies evolutionary game theory to examine the impact of government subsidies on the adoption of blockchain technology by healthcare institutions.

First, the authors analyze the interests of government administration departments and healthcare institutions separately in regards to blockchain adoption. Subsequently, the authors develop the payoff matrix of both participants and construct the evolutionary game model. And then, the authors calculate the replication dynamic equations and analyze the decision evolution of both participants through the replication dynamic equations and numerical experiments.

The numerical experiments demonstrate that government subsidies are effective in encouraging healthcare institutions to adopt blockchain technology. The study also reveals the necessary amount of subsidy required to guide healthcare institutions towards adoption. Additionally, the validity of the evolutionary game model in analyzing the interaction between governments and healthcare institutions is confirmed by the results.

Blockchain adoption in the healthcare industry differs from other emerging technologies, as there is the potential for it to reduce revenue for healthcare institutions. This study contributes to the analysis of theoretical models for promoting blockchain in the healthcare industry through subsidies. Additionally, it demonstrates the potential of evolutionary game theory in analyzing the adoption of blockchain technology, and the interaction between governments and healthcare institutions.

This study aims to investigate the process of developing loyalty in the Chinese mobile health community from the information seeking perspective.

A covariance-based structural equation model was developed to explore the mobile health community loyalty development process from information seeking perspective and tested with LISREL 9.30 for the 191 mobile health platform user samples.

The empirical results demonstrate that the information seeking perspective offers an interesting explanation for the mobile health community loyalty development process. All hypotheses in the proposed research model are supported except the relationship between privacy and trust. The two types of mobile health community loyalty—attitudal loyalty and behavioral loyalty are explained with 58 and 37% variance.

This paper has brought out the information seeking perspective in the loyalty formation process in mobile health community and identified several important constructs for this perspective for the loyalty formation process including information quality, communication with doctors and communication with patients.

The purpose of this paper is to understand the effect of platform characteristics (i.e. media richness and interactivity) on individual perception (i.e. outcome expectations) and consequent behavioral response (i.e. user participation in online health communities (OHCs)) based on the stimulus-organism-response (S-O-R) model.

This study developed a research model to test the proposed hypotheses, and the proposed model was tested using partial least squares structural equation modeling (PLS-SEM) for which data were collected from 321 users with OHC experience using an online survey.

The empirical results show the following: (1) the three dimensions of media richness significantly affect the three outcome expectations, except that richness of expression has no significant effect on the outcome expectation of health self-management competence. (2) Human-to-human interaction significantly affects the three outcome expectations. Moreover, compared with human-to-human interaction, human-to-system interaction has a stronger impact on the outcome expectation of health self-management competence. (3) The three outcome expectations have a significant influence on user participation in OHCs.

This study extends the understanding about how platform characteristics (i.e. media richness and interactivity) motivate user participation in the context of OHCs. Drawing on the S-O-R model, this study reveals the underlying mechanisms by which media richness and interactivity are associated with outcome expectations and by which outcome expectations is associated with user participation in OHCs. This study enriches the literature on media richness, interactivity, outcome expectations and user participation in OHCs, providing insights for developers and administrators of OHCs.

This chapter aims to examine the comparative economic performance of the United States versus China in the digital era. This chapter uses the Threshold Autoregressive (TAR) model method in comparing economic performance. The US economy was shaken quite significantly in 2008 due to the subprime mortgage crisis. On the other hand, China’s economic performance continues to improve. Based on the estimation results of China’s economic performance which continues to increase with faster economic growth than the United States, it is found that China has resistance to shocks from the 1997 crisis, the 2008 subprime mortgage crisis, and the European debt crisis in 2011 and has the potential to compete with the United States as the dominant country in economic terms. China’s economic growth is getting faster and faster since 1979. It indicates that China’s economy can surpass the US economy, which currently owns the largest gross domestic product (GDP) in the world.

With respect to the multiple-attribute decision-making problem with subjective preference for a certain attribute whose weight-value range have been given over other attributes whose weight values are unknown, a method based on the mean value of the grey number is proposed to analyse the decision-making problem. This method is used to choose a supply-chain partner under the condition that the decision makers have a preference for a certain attribute of various alternatives. The paper aims to discuss these issues.

First, the middle value of the preferred attribute’s weight-value range is supposed to be its weight value according to the content of the mean value of the grey number. Second, to reflect the decision maker’s subjective preference information, an improved optimisation model that requests the minimum deviation between the actual and expected numerical value of each attribute is constructed to assess the attributes’ weights. Third, the correlated degree and the correlation matrix, which are determined by the weight values of all attributes, are used to rank all the alternatives.

This paper provides a method for making a decision when decision makers have a preference for a certain attribute from an array of various alternatives, and the range of the certain attribute’s weight value is given but the weight value of the other attributes is unknown. When applied to supply-chain partner selection, this method proves feasible and effective.

This method is feasible and effective when applied to supply-chain partner selection, and can be applied to other kinds of decision-making problems. This means it has significant theoretical importance and extensive practical value.

Based on the mean value of the grey number, an optimisation model is built to determine the importance degree of each attribute, then the correlated degree of each alternative is combined to rank all the alternatives. This method can suit the decision makers’ subjective preference for a certain attribute well.