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It is challenging to derive an appropriate tolerance relation for tolerance rough set-based classifiers (TRSCs). The traditional tolerance rough set employs a simple distance function to determine the tolerance relation. However, such a simple function does not take into account criterion weights and the interaction among criteria. Further, the traditional tolerance relation ignores interdependencies concerning direct and indirect influences among patterns. This study aimed to incorporate interaction and interdependencies into the tolerance relation to develop non-additive grey TRSCs (NG-TRSCs).

For pattern classification, this study applied non-additive grey relational analysis (GRA) and the decision-making trial and evaluation laboratory (DEMATEL) technique to solve problems arising from interaction and interdependencies, respectively.

The classification accuracy rates derived from the proposed NG-TRSC were compared to those of other TRSCs with distinctive features. The results showed that the proposed classifier was superior to the other TRSCs considered.

In addition to pattern classification, the proposed non-additive grey DEMATEL can further benefit the applications for managerial decision-making because it simplifies the operations for decision-makers and enhances the applicability of DEMATEL.

This paper contributes to the field by proposing the non-additive grey tolerance rough set (NG-TRS) for pattern classification. The proposed NG-TRSC can be constructed by integrating the non-additive GRA with DEMATEL by using a genetic algorithm to determine the relevant parameters.

Infrastructure projects play a vital role in the development of countries' economies. However, these projects, notably underground ones, are encountering growing claims as they are subject to many unknowns. Without having a clear insight into the causes of claims and their influence on one another, it is not possible to mitigate their adverse impacts on these projects' goals. To this aim, the main purpose of this study is to identify the critical paths leading to cost claims for underground metro projects.

First, causes of claims were identified through a literature review and then refined to 22 main causes according to the opinions of 148 Iranian experts. A qualitative system dynamics (SD) model was then provided to visualize how various causes interact with each other. The model was developed and validated by drawing on the expertise of industry-related experts, and the critical paths were finally recognized based on their frequency and severity indexes.

The findings revealed that each cause should be evaluated based on their connections with others as there are complex relations among the causes. It was also shown that the major claim paths among others were initiated due to ambiguous contract clauses, unforeseen physical conditions and land handover problems.

The findings of this study provide a better insight into how various causes of claims for underground metro projects are related to each other and can eventually assist project managers of different parties, even in other countries, to select proportional strategies to mitigate their adverse impacts.

Five different structural reactive dyes (Reactive Brilliant Blue K-3R, Everacion Blue H-ERD, Moderzol Blue FBR, Atuzol Black B and Moderzol Blue HEGN) were treated with laccase (Denilite II US) in order to determine the optimum decolouration conditions. The experiments showed that laccase had distinct decolouration effects on these five dyes. Under optimum conditions, the colour removal rates of Everacion Blue H-ERD and Moderzol Blue HEGN were over 90%. Furthermore, the effects of different additives, such as acid ion, metal ion, and surfactants on the decolouration rate of Reactive Brilliant Blue K-3R were discussed. The results show that the decolouration rate is significantly promoted through the addition of Cu²⁺ and Al³⁺, while it is inactivated with Fe²⁺ and ion surfactants. Moreover, the COD removal rates of the five dyes are more than 75%.

With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues.

In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users.

Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter.

This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc.