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In the new stage of promoting high-quality economic development, the effect of the transformation of development momentum and the ability of sustainable development has become the key factors for the competitiveness of provinces in China. Especially in the context of the impact of COVID-19 and the obstacles of world trade protectionism, the sustainability of development performance is increasingly important. In the past, when evaluating the development performance of various provinces in China, a single index weight was usually used. In view of evaluation criteria, the lack of consideration of regional differentiation factors would result in the evaluation results deviating from reality. This paper introduces the entropy weight method to determine the weight of regional indicators of differentiated development. Based on the space-time probability function, a grey clustering evaluation model of regional development performance is constructed to conduct a comprehensive grey evaluation of the development performance of various provinces in China from 2009 to 2019. It is found that the new evaluation model can correct the deficiencies of similar probability functions and single index weight and obtain more accurate evaluation results. It’s found that the development performance evaluation results of each province are always in a dynamic adjustment process, which needs to be verified with the help of subsequent expansion analysis.

Abstract Background To meet the needs of diversified pension services and the construction of aged care institutions is one of the urgent livelihood issues in China. Under the major national needs of pension and care for the elderly, it is necessary to optimize the allocation of pension and care for the elderly resources, and formulate operational coping strategies and optimization plans. It is of great significance to deal with the urgent problem of population aging in a timely and scientific way. Methods The economic benefits and social costs of aged care institutions are regarded as objective functions. To satisfy the economic benefits, it is necessary to reduce the social costs, and its construction quantity can only be an integer. The multi-objective genetic algorithm is improved with integer programming, and the global optimal solution is achieved after several times of searching. Under the multi-objective optimization model, the improved genetic algorithm was combined with the superior and inferior solution distance method to solve the genetic algorithm, and the corresponding objective function value was obtained after rounding. Finally, Pareto optimal solution set is selected by multi-objective decision, and the result of construction planning is obtained. Results Based on multi-attribute decision analysis and taking three years as the construction cycle, the planning scheme of aged care institutions construction in each district of Nanjing in the next 15 years was proposed. In addition, considering the intelligent trend of future pension, the proportion of home-based pension is increasing year by year, and the model is promoted to meet the needs of diversified pension services. With the gradual increase of the elderly population in Nanjing, the proportion of intelligent home-based care has been increasing year by year since 2027, and the construction demand of aged care institutions has also increased. The number of construction at all levels rises gradually in each cycle, and the increase is more obvious after 2027. Conclusions The layout and planning of aged care institutions proposed in this paper not only considers economic and environmental benefits, but also combines the current situation of aged care institutions in various districts of Nanjing. The model is reasonable and effective, and has practical application value. It will help China optimize the allocation of elderly care resources under the target of active aging, and scientifically and comprehensively deal with the problem of population aging.

Complex engineered systems with various components and dynamic behaviors are connerstones to develop resilient cities and societies. These systems are robust but also vulnerable to adverse events and inevitably suffer performance degradation. An immediate question would be, “how can we manage and improve the resilience of a complex engineered system?”. This study proposes a quantitative framework to assess the resilience of complex engineered systems. The proposed framework focuses on figuring out the impact of functionality implementation on the capability of complex engineered systems to anticipate, absorb, adapt to, and restore from disruptive events. It is composed of three parts, including functionality analysis, performance evaluation, and resilience measure. Firstly, various functions are analyzed at the system level, where a functional tree is employed to investigate the relationship between functions. Then the actual performance of the system is evaluated while uncertain implementation of system functionality is considered. Finally, system resilience is measured from the perspectives of anticipation, absorption, adaptation, and restoration. Anticipation, absorption, adaptation, and restoration are critical capacities of complex engineered systems to ensure normal operation in the event of disruptions. The proposed framework provides a general approach for resilience assessment of complex engineered systems, which figures out functionality implementation and system performance under uncertainty.

Today’s challenges to sustainability are explored through a complex combination of interdisciplinary topics that explore various interactions between economic, social, and environmental systems that further contribute to existing uncertainties. Solving complex/dynamic sustainability constraints does not demand exclusively technical and practical methods, as it is equally important to have a profound conceptual understanding of the origins of such challenges. The purpose of this study was to investigate the challenges to the sustainable development process from the perspective of the philosophy of grey systems theory (GST). GST considers inherent defects and shortcomings in human understanding/knowledge and identifies the roots of uncertainty. The study concentrates on the sustainable development process, highlighting the ways in which GST explains the causes and sources of uncertainty in this process. It is emphasized that sustainability cannot be achieved without intentional human intervention, and that international collaboration is vital in solving sustainability problems. Uncertainty and challenges to sustainable development stem from human grey understanding and knowledge. This problem makes it difficult for humans to understand and model dynamicity, to strike a balance between different spheres of science, and to have an objective view of reality due to the dependence of knowledge on thinking paradigms and values. These shortcomings ultimately bring about value conflicts, different understandings of risks, and impediments to international collaboration and agreement. Finally, the study explains that uncertainty arises from incomplete understanding and grey knowledge, and that uncertainty undermines the prediction of outcomes. Furthermore, delays inherent in interactions and the impacts of diverse systems on the world increase uncertainty and complicate decision- and policymaking in improvement projects. In their efforts to implement their decisions and policies, humans also encounter various limitations in terms of their capacities, resources, and facilities. The application of GST-based approaches to the operational area is also discussed.

Purpose – The purpose of this paper is to propose a framework for data analytics where everything is grey in nature and the associated uncertainty is considered as an essential part in data collection, profiling, imputation, analysis and decision making. Design/methodology/approach – A comparative study is conducted between the available uncertainty models and the feasibility of grey systems is highlighted. Furthermore, a general framework for the integration of grey systems and grey sets into data analytics is proposed. Findings – Grey systems and grey sets are useful not only for small data, but also big data as well. It is complementary to other models and can play a significant role in data analytics. Research limitations/implications – The proposed framework brings a radical change in data analytics. It may bring a fundamental change in our way to deal with uncertainties. Practical implications – The proposed model has the potential to avoid the mistake from a misleading data imputation. Social implications – The proposed model takes the philosophy of grey systems in recognising the limitation of our knowledge which has significant implications in our way to deal with our social life and relations. Originality/value – This is the first time that the whole data analytics is considered from the point of view of grey systems.

This paper extends the existing absolute grey relational analysis (Absolute GRA) model, also called the absolute degree grey incidence analysis (ADGIA) model. By working on the limitations of the existing Absolute GRA model, the proposed model, named Bidirectional Absolute GRA (BAGRA) model, is better equipped to handle uncertain systems being represented by uncertain data. It is suitable for simultaneous handling of both linear and nonlinear arrays of data with both consistent and inconsistent directions of motion. The model introduces a bidirectional grey relational degree that is a composite measure of both direction and strength of the relationship. The study also introduced two scales to test the appropriateness of collected data to measure the confidence level on the information extracted from that data. In the end, after the pilot testing, the model is being applied on an R&D project management case from a real world. The work is important both theoretically and practically, especially for the data analysts concerned with the uncertain relationships between different data arrays, in general, and for the grey systems analysts, in particular.

Effectiveness evaluation of LEO satellite communication system can not only guide the optimization of constellation program, but also play an important role in the stage of system design. In order to ensure the accuracy of the evaluation results, it is necessary to consider the impact of uncertain information on effectiveness evaluation, whether it is due to poor information or complex system. In order to solve this problem, this paper introduces birth and death process into satellite communication system. Firstly, according to the limit distribution of grey birth and death process with continuous time and limited state, we establish the availability model of the system. Then, the Kolmogorov forward equation is used to characterize the dependability of the system during operation. After that, a capability evaluation index system based on “four domains” is constructed. The attribute values of the index are unified into generalized standard interval grey numbers, and the weight allocation model of GSIGN-AHP-ME is established.The system capability is calculated according to the full probability formula. Subsequently, taking constellation X as an example, the system effectiveness is obtained by using the G-BDP-ADC model constructed in this paper. Finally, by comparing with the fuzzy evaluation method, it shows that the G-BDP-ADC model constructed in this paper is better than the fuzzy evaluation method, and can describe the LEO satellite communication system in the context of poor information more appropriately.

The Distinguishing Coefficient (ξ) is an important parameter of Grey Relational Analysis (GRA), a flagship multi-criteria decision making (MCDM) model of Grey System Theory, an intelligent and multifaceted field developed by Chinese scientists in 1980s. However, the scholars widely assume ξ = 0.5. The current study questions this practice. Also, some scholars have argued that the variation in ξ doesn’t influence the ranking of the factors through GRA. On contrary, the study demonstrates, the variation in ξ can influence the ranking. This has been shown through a case involving primary data concerning the perceived relative importance of Project Management Knowledge Areas (PMKAs). This study is significant for the analysts of uncertain systems, represented by grey or fuzzy systems, who intend to use GRA for intelligent multi-criteria decision making. It encourages ξ – driven sensitivity analysis of GRA model before interpreting the results. The study reveals, by tailoring the value of ξ a point can be achieved where the ranking obtained through GRA can be made most comparable to the other MCDM methods. For comparative analysis of the GRA based results the study deployed three other MCDM techniques; Analytic Hierarchy Process, Best Worst Method and Simple Additive Weighting.

Telehealth, as an indispensable means of technical support in the Healthy China Strategy, currently has less than 20 percent adoption rate in China despite a great deal of government policies and investments. In the current study, to analyse the influencing factors behind doctors’ and patients’ adoption of telehealth, an asymmetric dynamic evolutionary game model of doctor-patient behaviour selection was established. Based on the model solution, the evolutionarily stable strategies that emerge in different situations were analysed. The results show that it is difficult for the adoption of telehealth in China to keep pace with coverage due to the “dual low” nature of telehealth: both doctors’ utility from telehealth and patients’ telehealth cost threshold are too low to incentivize adoption. The strategy to promote the adoption of telehealth in China should include providing adequate training for doctors and patients on the use of telehealth technology, rewarding doctors who provide telehealth services and raising the threshold cost of patient’s telehealth adoption.

The purpose of this paper is developing a method to choose an appropriate Computerized Maintenance Management System (CMMS) using Multiple Criteria Decision Making (MADM) for a dairy company. Among different methods, the grey group TOPSIS and fuzzy group VIKOR methods were selected. TOPSIS is a Technique for order of Preference by Similarity to Ideal Solution and VIKOR technique is a method based on compromise programming. The data in this article were gathered using the interviews with the company’s managers and elites in the field of maintenance. Then, out of five types of maintenance software, the best ones were selected using these two techniques and finally the results were compared with each other. In the selection process, 13 sub-criteria were introduced under 5 main criteria. These criteria were selected out of a huge number of criteria using the studies of others and by consulting with the company’s managers and experts. We have tried to make the choices in a way that the majority of aspects could be considered. This paper helps the maintenance managers in decision-making related to choosing the CMMS software in uncertainty environment. Using two different fuzzy and grey approaches and comparing the results can lead to appropriate selection and improve the confidence in decision-making. Maintenance planning issues are among the important issues in industrial production systems. Maintenance systems have basically made remarkable progress in recent years. The increase of companies’ competitive pressure on the one hand and the close relationship between maintenance activities and companies’ core activities, on the other hand, have encouraged companies to use the software in order to manage their maintenance activities.

OBJECTIVE: In this study, a novel grey self-memory coupling model was developed to forecast the incidence rates of two notifiable infectious diseases (dysentery and gonorrhea); the effectiveness and applicability of this model was assessed based on its ability to predict the epidemiological trend of infectious diseases in China. METHODS: The linear model, the conventional GM(1,1) model and the GM(1,1) model with self-memory principle (SMGM(1,1) model) were used to predict the incidence rates of the two notifiable infectious diseases based on statistical incidence data. Both simulation accuracy and prediction accuracy were assessed to compare the predictive performances of the three models. The best-fit model was applied to predict future incidence rates. RESULTS: Simulation results show that the SMGM(1,1) model can take full advantage of the systematic multi-time historical data and possesses superior predictive performance compared with the linear model and the conventional GM(1,1) model. By applying the novel SMGM(1,1) model, we obtained the possible incidence rates of the two representative notifiable infectious diseases in China. CONCLUSION: The disadvantages of the conventional grey prediction model, such as sensitivity to initial value, can be overcome by the self-memory principle. The novel grey self-memory coupling model can predict the incidence rates of infectious diseases more accurately than the conventional model, and may provide useful references for making decisions involving infectious disease prevention and control.

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1,k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1,k) model. The traditional grey model’s weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1,k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.

As limits of time, labors and expenses, observed data usually have the characteristic of small sample sizes in development test program. Redesigns or corrective actions can result in changes of reliability for equipments. We propose an improved GM(1,1) model to predict reliability growth in this paper. First, a newly initial condition in time response function is set in this improved GM(1,1) model. The newly initial condition is comprised of the first item and the last item of a sequence which is generated from applying the first-order accumulated generation operator to a sequence of raw data. Then the improved model can express the principle of new information priority well and improve prediction precision through fully applying new information in raw data. Secondly, we make use of the improved model to predict reliability growth in a numerical example. The comparison of predicted reliability growth curve from the improved GM(1,1) model and that from the Lloyd-Lipow model indicates that the improved GM(1,1) model is much better than the Lloyd-Lipow model for the reliability growth prediction.

This study aimed to identify and rank the barriers faced by disabled elderly in China while accessing eHealth primary care services. Primary data were collected from the disabled elderly based on technological, individual, relational, environmental, and organizational constructs. The Dynamic Grey Relational Analysis (DGRA) and Multiple-criteria Decision-making (MCDM) based TOPSIS techniques were used to identify and rank the barriers. We found that the most significant barrier was "aging limitation (reduction in hearing, sight, memory, and fine motor control)" in both (DGRA and MCDM) cases. The Kruskal-Wallis test was used to investigate the significance of this barrier in different age groups of disabled elderly. We found no significant differences among the three age groups of disabled elderly, which shows that the barrier "aging limitation (reduction in hearing. sight, memory, and fine motor control) " is the most significant barrier at each age group (when age ≥ 60) of disabled elderly. The average value of Grey Relational Grades (GRGaverage) and the sorting outcomes of the MCDM of the construct individual were higher than those of all other constructs. This study is the first of its kind to apply the DGRA. MCDM and KWT to expose the barriers while accessing eHealth services for the disabled elderly in China. [ABSTRACT FROM AUTHOR]