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The purpose of this paper is to take advantage of Internet of Things (IoT) for intelligent route programming of crowd emergency evacuation in metro station. It is a novel approach to ensure the crowd safety and reduce the casualties in the emergency context. An evacuation route programming model is constructed to select a suitable evacuation route and support the emergency decision maker of metro station.

The IoT technology is employed to collect and screen information, and to construct an expert decision model to support the metro station manager to make decision. As a feasible way to solve the multiple criteria decision-making problem, an improved multi-attributive border approximation area comparison (MABAC) approach is introduced.

The case study indicates that the model provides valuable suggestions for evacuation route programming and offers practical support for the design of an evacuation route guidance system. Moreover, IoT plays an important role in the process of intelligent route programming of crowd emergency evacuation in metro station. A library has similar structure and crowd characteristics of a metro station, thus the intelligent route programming approach can be applied to the library crowd evacuation.

The highlights of this paper are listed as followings: the accuracy and accessibility of the metro station’s real-time information are improved by integrating IoT technology with the intelligent route programming of crowd emergency evacuation. An improved MABAC approach is introduced to the expert support model. It promotes the applicability and reliability of decision making for emergency evacuation route selection in metro station. It is a novel way to combine the decision-making methods with practice.

The purpose of this paper is to propose a consensus method for multi-attribute group decision-making (MAGDM) problems based on preference-approval structure and regret theory, which can improve the efficiency of decision-making and promote the consensus level among individuals.

First, a new method to obtain the reference points based on regret theory and expert weighting method is proposed. Second, a consensus reaching method based on preference-approval structure is proposed. Then, an adjustment mechanism to further improve the consensus level between individuals is designed. Finally, an example of the assessment of elderly care institutions is used to illustrate the feasibility and effectiveness of the proposed method.

The feasibility and validity of the proposed method are verified by comparing with the advanced two-stage minimum adjustment method. The compared results show that the proposed method is more consistent with the actual situation.

This paper presents a consensus reaching method for MAGDM based on preference-approval structure, which considers the avoidance behaviors of individuals and reference points. Decision makers (DMs) can use this approach to rank and categorize alternatives while further increasing the level of consensus among them. This can further help determine the optimal alternative more efficiently.

A new MAGDM problem based on the combination of regret theory and individual reference points is proposed. Besides, a new method of obtaining experts' weights and a consensus reaching method for MAGDM based on preference-approval structure are designed.

Consistency and consensus are two important research issues in group decision-making (GDM). Considering some drawbacks associated with these two issues in existing GDM methods with intuitionistic multiplicative preference relations (IMPRs), a new GDM method with complete IMPRs (CIMPRs) and incomplete IMPRs (ICIMPRs) is proposed in this paper.

A mathematically programming model is constructed to judge the consistency of CIMPRs. For the unacceptably consistent CIMPRs, a consistency-driven optimization model is constructed to improve the consistency level. Meanwhile, a consistency-driven optimization model is constructed to supplement the missing values and improve the consistency level of the ICIMPRs. As to GDM with CIMPRs, first, a mathematically programming model is built to obtain the experts' weights, after that a consensus-driven optimization model is constructed to improve the consensus level of CIMPRs, and finally, the group priority weights of alternatives are obtained by an intuitionistic fuzzy programming model.

The case analysis of the international exchange doctoral student selection problem shows the effectiveness and applicability of this GDM method with CIMPRs and ICIMPRs.

First, a novel consistency definition of CIMPRs is presented. Then, a consistency-driven optimization model is constructed, which supplements the missing values and improves the consistency level of ICIMPRs simultaneously. Therefore, this model greatly improves the efficiency of consistency improving. Experts' weights determination method considering the subjective and objective information is proposed. The priority weights of alternatives are determined by an intuitionistic fuzzy (IF) programming model considering the risk preference of experts, so the method determining priority weights is more flexible and agile. Based on the above theoretical basis, a new GDM method with CIMPRs and ICIMPRs is proposed in this paper.

When integrating smart elements offered by emergent technologies, libraries are facing the challenges of technological renovation and maintaining their operation using emerging technology. Given the importance of smart library, new technologies are needed in building new libraries or renovation of existing libraries. The purpose of this paper is to propose a risk warning system for library construction or renovation in the aspect of risk management.

The proposed Internet of Things (IoT)-based system consists of sensors that automatically monitor the status of materials, equipment and construction activities in real time. AI techniques including case-based reasoning and fuzzy sets are applied.

The proposed system can easily track material flow and visualize construction processes. The experiment shows that the proposed system can effectively detect, monitor and manage risks in construction projects including library construction.

Compared with existing risk warning systems, the proposed IoT-based system requires less data for making dynamic predictions. The proposed system can be applied to new builds and renovation of libraries.

The purpose of this paper is to present a hybrid intuitionistic fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method, called intuitionistic fuzzy ordered weighted averaging weighted averaging (OWAWA) distance TOPSIS (IFOWAWAD-TOPSIS) method for intuitionistic fuzzy multiple-criteria decision making (MCDM) problems.

Based on the OWAWA operator, the authors develop the intuitionistic fuzzy OWAWA distance measure, then the IFOWAWAD-TOPSIS method is obtained by using the IFOWAWAD and traditional TOPSIS.

The developed IFOWAWAD-TOPSIS method can overcome the drawback of traditional TOPSIS method that cannot consider both the subjective information of attributes and the attitudinal character of decision maker.

Clearly, this paper is devoted to the OWA operator, MCDM and intuitionistic fuzzy theory.

The developed method is applicable in a wide range of situations such as decision-making, statistics, engineering and economics. A numerical example concerning investment selection is given to illustrate the practicability and usefulness of the proposed approach.

This paper fulfils an identified need to study how to make a decision considering both the subjective information of attribute and the attitudinal character of decision maker in intuitionistic fuzzy environment.

This study aims to identify the critical factors (barriers and drivers) influencing the adoption of cloud computing (ACC) in the manufacturing sector in India.

In this study, a mixed methodology approach is used. Interviews are conducted to investigate factors (drivers and barriers) influencing the ACC, which are further categorized as controllable determinants (weaknesses and strengths) and uncontrollable determinants (threats and opportunities) using a SWOT analysis. Fuzzy analytic hierarchy process (FAHP) has been utilized to highlight the most critical drivers as well as barriers. Finally, decision-making trial and evaluation laboratory (DEMATEL) has been used to find the cause-effect relationships among factors and their influence on the decision of adoption.

The manufacturing sector is in the digital and value change transformation phase with Industry 4.0, that is, the next industrial revolution. The 24 critical factors influencing ACC are subdivided into strengths, weaknesses, opportunities and threats. The FAHP analysis ranked time to market, competitive advantage, business agility, data confidentiality and lack of government policy standards as the most critical factors. The cause-effect relationships highlight that time to market is the most significant causal factor, and resistance to technology is the least significant effect factor. The results of the study elucidate that the strengths of ACC are appreciably more than its weaknesses.

This study couples the technology acceptance model (TAM) with technology-organization-environment (TOE) framework and adds an economic perspective to examine the significant influences of ACC in the Indian manufacturing sector. Further, it contributes to the knowledge of ACC in general and provides valuable insights into interrelationships among factors influencing the decision and strategies of adoption in particular.

This is the first scholarly work in the Indian manufacturing sector that uses the analysis from SWOT and FAHP approach as a base for identifying cause-effect relationships between the critical factors influencing ACC. Further, based on the extant literature and analysis of this work, an adoption framework has been proposed that justifies that ACC is not just a technological challenge but is also an environmental, economic and organizational challenge that includes organizational issues, costs and need for adequate government policies.

Assessing the failure probability of engineering structures is still a challenging task in the presence of various uncertainties due to the involvement of expensive-to-evaluate computational models. The traditional simulation-based approaches require tremendous computational effort, especially when the failure probability is small. Thus, the use of more efficient surrogate modeling techniques to emulate the true performance function has gained increasingly more attention and application in recent years. In this paper, an active learning method based on a Kriging model is proposed to estimate the failure probability with high efficiency and accuracy.

To effectively identify informative samples for the enrichment of the design of experiments, a set of new learning functions is proposed. These learning functions are successfully incorporated into a sampling scheme, where the candidate samples for the enrichment are uniformly distributed in the n-dimensional hypersphere with an iteratively updated radius. To further improve the computational efficiency, a parallelization strategy that enables the proposed algorithm to select multiple sample points in each iteration is presented by introducing the K-means clustering algorithm. Hence, the proposed method is referred to as the adaptive Kriging method based on K-means clustering and sampling in n-Ball (AK-KBⁿ).

The performance of AK-KBⁿ is evaluated through several numerical examples. According to the generated results, all the proposed learning functions are capable of guiding the search toward sample points close to the LSS in the critical region and result in a converged Kriging model that perfectly matches the true one in the regions of interest. The AK-KBⁿ method is demonstrated to be well suited for structural reliability analysis and a very good performance is observed in the investigated examples.

In this study, the statistical information of Kriging prediction, the relative contribution of the sample points to the failure probability and the distances between the candidate samples and the existing ones are all integrated into the proposed learning functions, which enables effective selection of informative samples for updating the Kriging model. Moreover, the number of required iterations is reduced by introducing the parallel computing strategy, which can dramatically alleviate the computation cost when time demanding numerical models are involved in the analysis.

This study aims to examine the impact of intellectual capital (IC) and its three components (human, structural and relational capital) on corporation performance in the Chinese transportation industry. In addition, this study also investigates auditor characteristics (both Big-N and non-Big-N auditors) as a moderating role to examine the relationship between IC and corporate performance.

The data include 398 firm-year observations of transportation companies listed on the Shanghai and Shenzhen Stock Exchange from 2011 to 2020. Value-added intellectual coefficient (VAIC) model and its modified version (MVAIC) are applied to measure IC efficiency. Finally, the fixed effects regression analysis is used to mitigate the endogeneity issue. To investigate the moderating effect of auditor characteristics, the authors divide the samples based on the clients audited by Big-4 and non-Big-4 firms.

This study reveals that IC can enhance firm performance in China’s transportation sector. Overall, findings indicate that on the whole, IC has a positive and significant impact on corporation profitability and productivity. Human capital and physical and financial assets (capital employed) play highly important roles, but structural capital has no significant impact. The authors also found that auditor characteristics play an important moderating role in the connection between IC and corporate performance. For example, the positive association between IC and corporate performance is more pronounced when Big-4 auditors audit client firms. At the same time, the authors found a negative relationship between IC and firm performance when non-Big-4 auditors audit client firms.

Managers must understand that several components of IC have a total effect on corporate financial performance. Therefore, managers can dedicate more resources to such components based on the performance outcomes to emphasize their business strategies.

This study is the first empirical analysis of the impact of IC and its components on corporation performance in the transportation sector in China, an emerging market. Previous studies mainly focus on developed countries’ high technology and financial industries sectors but the impact of IC in transportation industry largely remains unknown. Thus, the present findings contribute to IC literature by revealing several underlying mechanisms by which the components of IC help achieve good firm performance.

The purpose of this paper is to investigate the impact of intellectual capital (IC) and its components (human, structural, relational and innovation capitals) on financial performance (FP) at different life cycle stages.

The study uses the data from Chinese manufacturing listed companies during 2014–2018. The modified value added intellectual coefficient (MVAIC) model is employed as the measurement of IC efficiency. Finally, multiple regression analysis is used to test the research hypotheses.

This study shows that the impact of IC on FP is different across life cycle stages. Specifically, at the birth stage, human capital (HC), structural capital (SC) and innovation capital (INC) have a positive impact on FP. At the growth and mature stages, all IC components contribute to FP improvement. HC and SC play an important role at the revival stage, while only HC positively affects FP at the decline stage.

The findings may help corporate managers to make optimal strategies to improve FP by effective utilization of IC resources in the complex and competitive business environment. Meanwhile, companies can invest in the core elements of IC at different stages of development, so as to maximize the contribution of IC to company value.

This is among the few studies to explore the impact of IC on FP of manufacturing listed companies in the Chinese context from the perspective of life cycle. It also makes novel contributions in measuring IC by the MVAIC model with the inclusion of relational capital and INC that are largely neglected in previous research.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.