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The aim of this paper is to review the current situation and existing problem, establish investment benefits evaluation indicator system and introduce synthetic approach degree containing Hamming approach degree, Euclid approach degree and gray correlation degree to improve the shortage of Euclidean distance in traditional TOPSIS method, and the evaluation result is strengthened by multiplication rule. This paper aims to solve the distribution network investment decision-making problem and construct a comprehensive distribution network investment benefit indicator system, which is more suitable for China distribution network characteristics.

This study develops improved TOPSIS methods for decision maker in the power distribution network market and uses an example to prove its effectiveness and superiority in practice which can realize the combination of theory and practice.

The research shows that the investment evaluation indicator system built in present paper covers more investment benefit influencing factors (such as qualified rate of trunk cross-section, pass rate of N-1 lines), and the evaluation result obtained by improved TOPSIS method is more efficient and persuasive.

The study can help investors evaluate distribution network project more efficient, and make contribution to the choice of distribution cases with similar investment benefits.

To improve power system peak dispatching ability, connecting energy-storage device such as electric vehicle (EV) and regenerative electric heater (REH) to power grid is a good choice.

This paper establishes a multi-energy combined peak dispatching system MCPDS which includes EV, REH and wind power. The matter-element extension model based on improved variable weight theory is applied to evaluate MCPDS synthetic benefit.

The research shows that the MCPDS established in this paper performs excellently in security benefit, economic benefit, social benefit and environmental benefit.

With the assistance of energy storage devices such as EV and REH, the electrical system peak dispatching ability and power system operation efficiency has improved. More devices with energy-storage ability should be introduced into electrical power system to improve its synthetic benefit.

Emerging technologies, such as virtual reality (VR), have been influencing both the tourism supply side and tourists alike. The purpose of this study is to analyse VR research in tourism and to provide a comprehensive state-of-the-art review. As the technological connotation of the term VR has been changing and encompasses various VR systems with different capabilities, this paper aims to provide a systematic and structured overview. The overall objective of this paper is to contribute to a thorough understanding of VR research in tourism.

This paper comprehensively reviews and analyses existing literature on VR in tourism, published from 1994 to February 2018. Using a wide variety of sources, these papers were examined so as to give a state-of-the-art literature review and to deepen one’s understanding of the diverse applications of VR in a tourism context. This paper also presents a novel classification of different VR systems according to the level of immersion and depicts their respective technological capabilities.

The advent of new VR hardware necessitates a distinction for different VR systems applied in the tourism sector. Research conducted during the past three years has been focussing on the application of head-mounted displays, which reflects the temporal development of VR technology. Regardless of the VR system, most studies examine VR as a marketing tool for promotion and communication purposes during the pre-travel phase, focussing on behavioural aspects. Advances in technology will yield new opportunities and application possibilities for the tourism industry.

The key contribution of this paper lies in its structural approach, which differentiates between non-, semi- and fully immersive VR systems in tourism, as well as the proposition of respective definitions. The concluding part of the paper proposes practical implications for tourism businesses together with directions for future studies.

It is of great importance for economy policy makers to comprehend the relationship between macroeconomic indicators and domestic savings, and to find out which indicator is more determinative on the dynamics of domestic savings. The purpose of this paper is to analyze the degree of relationship between Turkey’s domestic savings and selected macroeconomic indicators.

To examine the relationship, grey relational analysis (GRA) is applied together with the entropy method to determine the weight of the indicators according to the information level they provide. The analysis covers the data of the period from 1990 to 2014. In practice, however, the data set is used by dividing into two separate periods including before and after the 2001 crisis.

The results indicate that the unemployment rate and the gross domestic product (GDP) per capita growth stand out with a relatively high degree of relationship for the period before 2001. When examining the post-2001 period, current balance ratio and GDP growth are ascertained as indicators which have a high degree of relationship with domestic savings.

These indicators have different aspects affecting both public and private savings. Therefore, it may be beneficial to concentrate on these indicators when designing a policy in order to increase the domestic saving rate.

There are many econometric models used for investigating Turkey’s macroeconomic indicators and domestic savings causality. But before now, any study which investigates relationship between macroeconomic indicators and domestic savings by GRA could not be encountered. Using one of the newest developed theories (the grey systems theory) for this subject is the significance of this research.

The purpose of this paper is to explore a new approach to solve the problem of positive and negative offset in the calculation process of integral elements, then propose a series of new grey relational degree model for cross sequences.

The definitions of cross sequences and area elements have been proposed at first. Then the concept of difference degree between sequences has been put forward. Based on the definition of difference degree between sequences, various modified grey relational degree models for cross sequences have been proposed to solve the measurement problem of cross sequence correlation relationships.

(1) The new definition of cross sequences; (2) The area element; (3) Various modified grey relational degree models for cross sequences based on the definition of difference degree between sequences.

The grey relational analysis model of cross sequences is a difficult problem in grey relational analysis. The new model proposed in this article can effectively avoid the calculation deviation of grey relational analysis model for cross sequences, and reasonably measure the correlation between cross sequences. The new model was used to analyse the food consumer price index in Shaanxi Province, clarifying the relationship between different types of food consumer price indices, some interesting results that are not completely consistent with general economic theory were obtained.

The new definition of cross sequences, the area element and various modified grey relational degree models for cross sequences were proposed.

The purpose of this paper is to evaluate and rank the influence of internet public opinion of China’s Government work in 2015 by weighted absolute degree of grey incidence with TOPSIS.

This disaggregation method includes four main steps, determine the vector of weights for the factors by analytic hierarchy process, calculate the matrix of consistent effect measure, determine the weighted absolute degree of grey incidence with TOPSIS, rank and evaluate the events.

We get the ranking of internet public opinion of China’s Government work in 2015 by weighted absolute degree of grey incidence with TOPSIS. The result can be used for evaluating and ranking the influence of internet public opinion in China. The positive weighted absolute degree of grey incidence, the negative weighted absolute degree of grey incidence and the weighted absolute degree of grey incidence with TOPSIS have the same ranking results. The same ranking results show that the method of weighted absolute degree of grey incidence with TOPSIS has good consistency.

The weighted absolute degree of grey incidence with TOPSIS can be easily used for other evaluation.

The weighted absolute degree of grey incidence with TOPSIS is proposed and first used for evaluating and ranking the influence of internet public opinion of China’s Government work.

Nowadays, growth is the common target of all societies. But rather than growth, it is more important to ensure the sustainability of growth. Worldwide climate changes, damages to natural capital and financial crises necessitate the transition to green growth. The purpose of this paper is to examine the contribution of socio-economic context to green growth, which represents the sum of environmental and resource productivity, natural asset base, the environmental dimension of quality of life and technology.

The paper uses grey relational analysis together with the entropy method to examine the weight of 22 green growth indicators. The green growth indicators based on the compilation of the data from 36 Organization for Economic Co-operation and Development countries in 2015.

The results point out carbon dioxide emissions and environment-related technology are the most essential indicators in achieving green growth across the world.

It provides an objective evaluation of the green growth indicators that creates awareness-raising in green growth, enables the measurement of global developments and the determines opportunities and risks.

In this study, a hybrid rough and grey set-based rule model is designed for diagnosis of one type of blood cancer called multiple myeloma (MM). The grey clustering method is used to combine the same condition attributes and to improve the validity of the final model.

Some tools of the rough set theory (RST) and grey incidence analysis (GIA) are used in this research to analyze the serum protein electrophoresis (SPE) test results. An RST-based rule model is extracted based on the laboratory SPE test results of patients. Also, one decision attribute and 15 condition attributes are used to extract the rules. About four rule models are constructed due to the different algorithms of data complement, discretization, reduction and rule generation. In the following phases, the condition attributes are clustered into seven clusters by using a grey clustering method, the value set of the decision attribute is decreased by using manual discretizing and the number of observations is increased in order to improve the accuracy of the model. Cross-validation is used for evaluation of the model results and finally, the best model is chosen with 5,216 rules and 98% accuracy.

In this paper, a new rule model with high accuracy is extracted based on the combination of the grey clustering method and RST modeling for diagnosis of the MM disease. Also, four primary rule models and four improved rule models have been extracted from different decision tables in order to define the result of SPE test of patients. The maximum average accuracy of improved models is equal to 95% and related to the gamma globulins percentage attribute/object-related reducts (GA/ORR) model.

The total number of observations for rule extraction is 115 and the results can be improved by further samples. To make the designed expert system handy in the laboratory, new computer software is under construction to import data automatically from the electrophoresis machine into the resultant rule model system.

The main originality of this paper is to use the RST and GST together to design and create a hybrid rule model to diagnose MM. Although many studies have been carried out on designing expert systems in medicine and cancer diagnosis, no studies have been found in designing systems to diagnose MM. On the other hand, using the grey clustering method for combining the condition attributes is a novel solution for improving the accuracy of the rule model.

As the grey systems theory has been widely used in the field of sustainable development (SD) research, in the following, a short literature overview will be put forward, starting from the usage of these theories in the economic development, social inclusion and environmental protection contributions to the evolving process of SD during 2011–2021. The purpose of this paper is to identify some key studies from all the SD areas in which the grey systems can be used in order to open and to bring the researchers to new domains in which they can reveal their interest and in which they can successfully use the methods offered by the grey systems theory.

Using the search engine offered by the Google Scholar and the Web of Science (WoS), a literature review has been performed for the grey systems applications on SD research on both grey relational analysis (GRA) and grey forecasting. In addition, some grey evaluation theories – clustering evaluation models and grey target decision models – have also been presented.

Many grey models are widely used in the field of SD. Compared with other methods such as grey prediction, grey evaluation and decision-making model, GRA technology is the most used method, and the research using this method is more than three times that of all other methods.

The present paper identifies some of the most representative examples in which the grey system theory (GST) has been used, but, in the same time, there are a lot of studies that have not been mentioned here due to the lack of space.

The present paper focuses on the SD applications in which GST has been successfully used, bringing to the reader a general overview on this field and, in the same time, enables new research perspectives.

The purpose of this paper is to analyze the energy audit reports in order to define the most favorable factors affecting energy consumption of buildings. Since energy audit of buildings includes assessment of occupants comfort level in addition to the technical data of buildings so some rules are extracted to model the employees thermal comfort level in organization.

Some tools of RST and GIA are used in this research to analyze the energy consumption of official buildings. “Average energy consumption of building per year” is selected as a system characteristic in GIA and as a decision attribute in RST to show the behavior of buildings energy consumption. Ten technical sequences of buildings are chosen as relevant factors of behavior and conditional attributes in GIA and RST. In order to model the employees thermal comfort level in organization by RST, ten technical attributes of buildings are selected as condition attributes and thermal comfort level of employees is selected as decision attribute. Due to the different algorithms of data complement, discretization, reduction, and rule generation, four rule models are constructed. Cross-validation is used for evaluation of the model results and the best model is chosen with 62 rules and 99.8 percent of accuracy.

According to the results of GIA and RST, “Uncontrolled area of the building” has been diagnosed as the most important factor between other relevant factors/attributes and it has the greatest effect on energy consumption of building. Four rule models have been extracted from deferent decision tables in order to describe the thermal comfort level of employees in organization. The maximum number of rules relates to the conditional combination/GA model with 1263 rules and average accuracy of 99.7 percent and the minimum number relates to the conditional combination/Janson model with 62 rules and average accuracy of 99.8 percent.

The total observations for rule extraction is 81 and the results can be improved by further samples.

It shows that “Uncontrolled area of the building” is the most important factor/attribute to define the consumption of buildings and thermal comfort level of employees in organization.