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.  

In order to solve the decision-making problem that the attributive values are internal grey numbers and the attributive weights are unknown, this paper try to construct an entropy-weighted TOPSIS model based on the generalized greyness of interval grey number from the perspectives of proximity and equilibrium. Firstly, the properties of greyness distance are analyzed and the simplified formula for computing greyness distance is given. Then, a method to determine entropy weight based on greyness distance is given, and an entropy weighted TOPSIS decision-making model is established. Finally, the constructed model is applied to selecting brackish water irrigation pattern of winter wheat in North China Plain, China, so to verify its feasibility and effectiveness. The results show that the model proposed in this paper not only fully utilizes the measurement information of interval grey numbers, but also overcomes the influence of subjective factors on weights, and provide a new method for decision-making of unknown attributive weights and attributive value with interval grey number, and the interval grey numbers coexist with the real numbers. The application examples show that the model proposed in this paper is feasible and valid. 

A new damping nonlinear grey multivariate seasonal forecasting power model DAFGM(1,N, , ) is proposed to solve the problem of small sample forecasting with seasonal, nonlinear, and uncertain system behavior characteristic sequence. Firstly, the seasonal moving filter is used to eliminate the seasonal characteristics of the original series. Then, according to the principle of "new information priority ", the damping accumulation coefficient is introduced, the unknown factors which are difficult to collect are simulated by introducing dummy variables, and a new seasonal forecasting model is constructed. Finally, the model is used to forecast the quarterly wind power generation in China. The results show that the model has good practicability and effectiveness.  

Epidemic models are widely used in financial risk prediction. The problems of nonlinear changes in infection rates and limited data samples in financial risk remain to be addressed. To this end, this paper proposes a nonlinear grey SIRS (abbreviated as GSIRS) model based on short-term data. This model employs a time-varying function to capture the nonlinear dynamics of infection rates, and integrates the system grey prediction model to analyze short-term data. Parameter optimization is achieved through the least square method and the whale optimization algorithm. The GSIRS model shows good prediction accuracy across three financial crisis datasets, with MAPE ranging from 3.379% to 4.981% for training sets and 2.913% to 3.212% for test sets. These values are significantly better than those of competition models. In addition, the CWC values of the interval prediction under the 95% confidence level of the model are 0.13, 0.14 and 0.33, respectively. The combination of excellent RMSE and STD metrics further proves the stable forecasting ability. Meanwhile, the sensitivity analysis shows that changes of infection rate have a 1-2 period lagged effect on the infected individual density.  

To address the problem of temperature errors in secondary instruments operating in high- and low-temperature environments, this paper proposed a temperature correction method based on the ARIMA–GM(1,1) model. First, a standard source was connected to a temperature secondary instrument placed in a high- and low-temperature circulation box. The errors between the measurements of the standard source and the secondary instrument could be calculated and obtained a set of error sequences. Second, the error sequences were used to establish an ARIMA model and obtained a set of predicted values. And the residual between the errors and the predicted values could be calculated. To improve the accuracy of the ARIMA model, a GM(1,1) residual correction model was established based on the residual sequences. Lastly, the ARIMA and the GM(1,1) models were combined to formulate an ARIMA–GM model that could perform error self-correction for the temperature secondary instrument. In application experiments, the model achieved smaller average relative errors than a traditional ARIMA and hybrid models. Finally, we developed the ARIMA–GM(1,1) model into a software and applied it to cases of actual detection. 

In this paper, a new fractional-order grey forecasting model with a temporal power term that can handle both annual and quarterly forecasting tasks for GDP is presented. The model's characteristic is that it has a dynamic simulation parameter, which can automatically adjust the structure of the model according to the need of the prediction task to achieve the purpose of accurate prediction. In addition, the fractional order parameter and power term parameter of the model play an important role in enhancing the adaptive performance of the model. In particular, an excellent intelligent optimization algorithm, the Ant lion optimizer, is used to solve the model's programming model to obtain the hyperparameters for modeling quickly. In this study, China's annual GDP and quarterly GDP are used as research objects to verify the validity of the new model. The experimental results show that all evaluation indicators of the proposed method are better than those of its competitors. Therefore, the model has some application value. 

Scientific and accurate forecast of ecological environmental water consumption (EEWC) in the upper Yangtze River basin is of major prominence to the sustainable development of the basin and the formulation of eco-environmental protection policies. Firstly, a two parameter variable weight buffer operator is used to pre-processing the system shock behavior sequence. Then, an improved grey model IGM4(λ,γ,t^a) with four background values is established, introducing power exponential terms and linear correction terms to characterize data series with mixed linear and nonlinear relationships. The particle swarm optimization (PSO) algorithm is employed to find optimal parameters. Additionally, the model’s effectiveness is evaluated by comparing the fitting values of models with other grey models. The final results demonstrate that the IGM4( λ,γ,t^a) performs best with mean absolute percentage error only 0.0199%. Finally, model IGM4( λ,γ,t^a) is utilized to predict the EEWC in the upper Yangtze River basin from 2023 to 2028. The reasonableness of the predicted results is analyzed, and related policy measures are put forward. 

Fractional-order grey prediction models have gained wide recognition for their computational efficiency and straightforward modeling mechanisms. However, their performance in seasonal forecasting tasks still needs improvement. To address this, this paper designs a novel optimization scheme and applies it to the representative fractional-order grey GM(1,1) model (FGM(r,1)) to advance research in this area. In this optimization scheme, the dummy variable is used to enable the model to directly handle seasonal time series, the discretization technique is employed to simplify the computational steps, and the Bernoulli parameter and the linearly weighted hybrid fractional-order accumulation strategy are used to enhance the model's fitting capability. To verify the effectiveness of the proposed method, the optimized model and some benchmark algorithms are used to model three quarterly data sets. The experimental results show that the optimized model can produce better performance, which verifies the effectiveness of this optimization scheme. 

As a new edition of Grey Systems Analysis by Professor Sifeng Liu is about to be published, I am great honored to write the preface for this classic work in the field of grey systems research. In the mid to late 20th century, human society began to move towards the information age.People are beginning to deeply realize that data analysis methods have become an indispensable skill for everyone.The characteristics and operating rules of various systems are like gold buried in a sea of sand, deeply concealed by the chaotic and complex data information, and there is an urgent need for effective scientific methods to explore and reveal.In response to the needs of the times, as a poverty information data analysis method, grey system theory has emerged. Grey system theory takes the "poor data" uncertain system with "some information known and some information unknown" as the research object. It mainly extracts valuable information through the mining of "some" known information, and realizes the correct description of the system operation behavior and evolution law, so that people can use mathematical models to analyze and assess the "poor data" uncertain system, then realize high-precision prediction, scientific decision-making and optimal control of the "poor data" uncertain system. Prof. Liu has been dedicated to grey system research for 40 years, and his series of original concepts and models have become classics in the field. Such as general grey numbers, simplified forms of grey numbers, and their algebraic systems; Construction and properties of sequence operators and practical buffer operators; A series of grey relational analysis models based on a global perspective; The grey evaluation model based on a mixed possibility function of endpoints and center points, a multiobjective weighted intelligent grey target decision-making model, and a two-stage grey decision-making model based on a kernel weight vector group; And various original poverty information data prediction models such as original difference models, mean difference models, discrete grey models, fractional order grey models, and self memory models proposed in collaboration with his students. Especially his seminal books, greatly promoted the dissemination and development of grey system theory. The Grey System Theory and Its Applications, first published in 1991, were deeply loved by readers. In 2024, Science Press released its 10th edition, which was rated as the first highly cited book in pandect of Natural Science by China National Knowledge Infrastructure. Multiple English versions, such as An Introduction to Grey System Theory(1998, IIGSS Academic Publisher, USA), Grey Information(2006, Springer London Ltd, UK), Grey Systems (2011, Springer-Verlag, DE), Grey Data Analysis (2016, Springer, SG), Grey Systems Analysis (2022, Springer, SG), are the first choice for scholars from all over the world to understand grey system theory and its research progress. Currently, scholars from over 130 countries or regions around the world have published papers on grey systems. My team has been conducting grey system theory research for over 20 years. And starting to publish papers related to grey systems in the early 21st century.We have successfully applied grey system methods and models to solve problems such as construction project evaluation and supplier selection, and proposed multiple combined grey models, such as COPRAS-G, ARAS-G, and EDAS-G, etc. This book will undoubtedly benefit more grey system theory learners and researchers as it is published in OA format with the support of the Excellent Academic Works Publishing Fund of Northwestern Polytechnical University. Grey system theory is a powerful tool for analyzing uncertain data in the era of big data. I look forward to its widespread dissemination worldwide, promoting the in-depth application of grey system theory in the fields of natural sciences, social sciences, and engineering technology.

In the era of big data, the paradigm of scientific research is undergoing fundamental changes. The Fourth Paradigm: DataIntensive Scientific Discovery which proposed by Jim Gray, a Turing Award winner, is increasingly becoming the mainstream paradigm in scientific research. The significant feature of big data is its low information density. The characteristics and operation rules of various systems are like gold buried in a sea of sand, deeply concealed by the big chaotic and complex uncertain data. In 1982, Professor Julong Deng founded the Grey System Theory, which is a distinctive method for modeling and analyzing uncertain data. Grey system theory takes the "poor data" uncertain system with "some information known and some information unknown" as the research object. It mainly extracts valuable information through the mining of "some" known information, and realizes the correct description of the system operation behavior and evolution law, so that people can use mathematical models to analyze and assess the "poor data" uncertain system, then realize high-precision prediction, scientific decision-making and optimal control of the "poor data" uncertain system. Prof. Liu has been dedicated to grey system research for 40 years. The series of concepts and models he proposed have become classics in this field. Such as kernel, degree of greyness of grey number, simplified form of grey number, general grey numbers and their algebraic systems; sequence operator, weakening and strengthening buffer operators; A series of grey relational analysis models based on a global perspective; The grey evaluation model based on mixed possibility function of endpoints and center points, a multi-objective weighted intelligent grey target decision-making model, and a two-stage grey decision-making model based on a kernel weight vector group; And various original poverty information data prediction models such as original difference models, mean difference models, discrete grey models, fractional order grey models, and self memory models proposed in collaboration with his students. These original achievements have greatly enriched the knowledge system of grey system theory. Various editions of his seminal book on Grey system theory have been published in different languages such as Chinese, English, Romanian and Korean. Hundreds of universities from around the world adopted them as textbooks. There are more than one million audiences of his books, videos and software of grey modeling. In 2024, he was selected as one of the top 0.05% Lifetime Highly Ranked Scholar in Systems Theory by Scholar GPS. His publications have been cited 51270 times with an H-Index of 95 in Huezhi Scholar. As a new edition of his book of Grey Systems Analysis is about to be published, I am great honored to write the foreword for this classic work. This book will undoubtedly benefit more grey system theory learners and researchers as it has been funded by Publishing Fund of Excellence Academic Works of NPU and will be published as OA book. It is expected that it will be widely spread around the world, promote the in-depth application of grey system methods and models, and benefit all mankind.