Noel Scott, Brent Moyle, Ana Cláudia Campos, Liubov Skavronskaya and Biqiang Liu
The purpose of this paper is to present the terms of grey clustering evaluation models.
Abstract
Purpose
The purpose of this paper is to present the terms of grey clustering evaluation models.
Design/methodology/approach
The definitions of basic terms about grey clustering evaluation models are presented one by one.
Findings
The reader could know the basic explanation about the important terms about various grey clustering evaluation models from this paper.
Practical implications
Many of the authors’ colleagues thought that unified definitions of key terms would be beneficial for both the readers and the authors.
Originality/value
It is a fundamental work to standardise all the definitions of terms for a new discipline. It is also propitious to spread and universal of grey system theory.
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In an urbanising world, neighbouring is perceived to be steadily losing significance and a remnant of the past. The same belief can also be found in China where rapid urbanisation…
Abstract
In an urbanising world, neighbouring is perceived to be steadily losing significance and a remnant of the past. The same belief can also be found in China where rapid urbanisation has had a tremendous impact on the social networks and neighbourhood life of urban residents. This chapter challenges the common perception of neighbouring in demise and argues that neighbouring remains an important form of social relationship, even if the meanings and role of neighbouring have changed. This chapter first charts the changing role of neighbouring from the socialist era to post-reform China. It then provides an account of four common types of neighbourhoods in Chinese cities – work-unit estates, traditional courtyards, commodity housing estates and urban villages – and considers how and why neighbouring in different ways still matters to them. In pre-reform socialist China, neighbourhood life and neighbouring comprised much of the daily social life of residents. Since the reform era, with the proliferation of private commodity housing estates, middle-class residents prioritise comfort, security and privacy, such that neighbouring levels have subsided. Nevertheless, in other neighbourhood types, such as work-unit housing estates, traditional courtyards and urban villages, neighbours still rely upon one another for various reasons.
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Xin Fan, Yongshou Liu, Zongyi Gu and Qin Yao
Ensuring the safety of structures is important. However, when a structure possesses both an implicit performance function and an extremely small failure probability, traditional…
Abstract
Purpose
Ensuring the safety of structures is important. However, when a structure possesses both an implicit performance function and an extremely small failure probability, traditional methods struggle to conduct a reliability analysis. Therefore, this paper proposes a reliability analysis method aimed at enhancing the efficiency of rare event analysis, using the widely recognized Relevant Vector Machine (RVM).
Design/methodology/approach
Drawing from the principles of importance sampling (IS), this paper employs Harris Hawks Optimization (HHO) to ascertain the optimal design point. This approach not only guarantees precision but also facilitates the RVM in approximating the limit state surface. When the U learning function, designed for Kriging, is applied to RVM, it results in sample clustering in the design of experiment (DoE). Therefore, this paper proposes a FU learning function, which is more suitable for RVM.
Findings
Three numerical examples and two engineering problem demonstrate the effectiveness of the proposed method.
Originality/value
By employing the HHO algorithm, this paper innovatively applies RVM in IS reliability analysis, proposing a novel method termed RVM-HIS. The RVM-HIS demonstrates exceptional computational efficiency, making it eminently suitable for rare events reliability analysis with implicit performance function. Moreover, the computational efficiency of RVM-HIS has been significantly enhanced through the improvement of the U learning function.
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Sifeng Liu, Qi Li and Yingjie Yang
The purpose of this paper is to present a novel synthetic index of two counts and mathematical model for researcher evaluation.
Abstract
Purpose
The purpose of this paper is to present a novel synthetic index of two counts and mathematical model for researcher evaluation.
Design/methodology/approach
A synthetic index L for researcher evaluation considering both the total number of other citations (C) and nonacademic impact (I) and a synthetic evaluation model are proposed in this paper. C and I are verified impact indexes. According to investigation by Delphi method, researchers are divided into five different classes of “below average,” “average,” “good,” “excellent” and “stellar.” The threshold values for counts C of grey class “stellar” are determined by deep investigation. The possibility functions of the two counts C and I on four grey classes of “below average,” “average,” “good” and “excellent” are built.
Findings
The novel synthetic index of two counts and mathematical model for researcher evaluation provide a better way to conduct researcher assessment.
Practical implications
The synthetic index L presented in this paper can be used to evaluate a researcher. It's more reasonable than the current research assessment indexes such as the number of publications and the numbers of so-called high-quality journal publications and the amount of granted funds and so on. The synthetic index L reflects the actual value created by a researcher. No artificial maneuver can change them significantly.
Originality/value
A synthetic index L for researcher evaluation considering both the total number of other citations (C) and nonacademic impact (I) and a synthetic evaluation model are proposed in this paper.
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Abstract
Presents a unified information systems theory where stochastic information, fuzzy information, rough information, grey information, unascertained information and white and black information are all special cases. A unified concept of information, named blind information, is introduced. Also, relevant mathematical representations of various types of information are presented.
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The purpose of this paper is to propose a new grey prediction model, GOFHGM (1,1), which combines generalised fractal derivative and particle swarm optimisation algorithms. The…
Abstract
Purpose
The purpose of this paper is to propose a new grey prediction model, GOFHGM (1,1), which combines generalised fractal derivative and particle swarm optimisation algorithms. The aim is to address the limitations of traditional grey prediction models in order selection and improve prediction accuracy.
Design/methodology/approach
The paper introduces the concept of generalised fractal derivative and applies it to the order optimisation of grey prediction models. The particle swarm optimisation algorithm is also adopted to find the optimal combination of orders. Three cases are empirically studied to compare the performance of GOFHGM(1,1) with traditional grey prediction models.
Findings
The study finds that the GOFHGM(1,1) model outperforms traditional grey prediction models in terms of prediction accuracy. Evaluation indexes such as mean squared error (MSE) and mean absolute error (MAE) are used to evaluate the model.
Research limitations/implications
The research study may have limitations in terms of the scope and generalisability of the findings. Further research is needed to explore the applicability of GOFHGM(1,1) in different fields and to improve the model’s performance.
Originality/value
The study contributes to the field by introducing a new grey prediction model that combines generalised fractal derivative and particle swarm optimisation algorithms. This integration enhances the accuracy and reliability of grey predictions and strengthens their applicability in various predictive applications.
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Bingjun Li, Shuhua Zhang, Wenyan Li and Yifan Zhang
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Tooraj Karimi and Mohamad Ahmadian
Competition in the banking sector is more complex than in the past, and survival has become more difficult than before. The purpose of this paper is to propose a grey methodology…
Abstract
Purpose
Competition in the banking sector is more complex than in the past, and survival has become more difficult than before. The purpose of this paper is to propose a grey methodology for evaluating, clustering and ranking the performance of bank branches with imprecise and uncertain data in order to determine the relative status of each branch.
Design/methodology/approach
In this study, the two-stage data envelopment analysis model with grey data is applied to assess the efficiency of bank branches in terms of operations. The result of grey two-stage data envelopment analysis model is a grey number as efficiency value of each branch. In the following, the branches are classified into three grey categories of performance by grey clustering method, and the complete grey ranking of branches are performed using “minimax regret-based approach” and “whitening value rating”.
Findings
The results show that after grey clustering of 22 branches based on grey efficiency value obtained from the grey two-stage DEA model, 6 branches are assigned to “excellent” class, 4 branches to “good” class and 12 branches to “poor” class. Moreover, the results of MRA and whitening value rating models are integrated, and a complete ranking of 22 branches are presented.
Practical implications
Grey clustering of branches based on grey efficiency value can facilitate planning and policy-making for branches so that there is no need to plan separately for each branch. The grey ranking helps the branches find their current position compared to other branches, and the results can be a dashboard to find the best practices for benchmarking.
Originality/value
Compared with traditional DEA methods which use deterministic data and consider decision-making units as black boxes, in this research, a grey two-stage DEA model is proposed to evaluate the efficiency of bank branches. Furthermore, grey clustering and grey ranking of efficiency values are used as a novel solution for improving the accuracy of grey two-stage DEA results.