Predicting of elderly population structure and density by a novel grey fractional-order model with theta residual optimization: a case study of Shanghai City, China.

BACKGROUND: Accurately predicting the future development trend of population aging is conducive to accelerating the development of the elderly care industry. This study constructed a combined optimization grey prediction model to predict the structure and density of elderly population. METHODS: In this paper, a GT-FGM model is proposed, which combines Theta residual optimization with fractional-order accumulation operator. Fractional-order accumulation can effectively weaken the randomness of the original data sequence. Meanwhile, Theta residual optimization can adjust parameter by minimizing the mean absolute error. And the population statistics of Shanghai city from 2006 to 2020 were selected for prediction analysis. By comparing with the other traditional grey prediction methods, three representative error indexes (MAE, MAPE, RMSE) were conducting for error analysis. RESULTS: Compared with the FGM model, GM (1,1) model, Verhulst model, Logistic model, SES and other classical prediction methods, the GT-FGM model shows significant forecasting advantages, and its multi-step rolling prediction accuracy is superior to other prediction methods. The results show that the elderly population density in nine districts in Shanghai will exceed 0.5 by 2030, among which Huangpu District has the highest elderly population density, reaching 0.6825. There has been a steady increase in the elderly population over the age of 60. CONCLUSIONS: The GT-FGM model can improve the prediction accuracy effectively. The elderly population in Shanghai shows a steady growth trend on the whole, and the differences between districts are obvious. The government should build a modern pension industry system according to the aging degree of the population in each region, and promote the balanced development of each region.

Distributed robust optimization for low-carbon dispatch of wind-thermal power under uncertainties.

Faced with the requirement of carbon emission reduction in power industry, low-carbon power dispatch involving various low-carbon approaches has been recognized as one of effective ways. Concentrate on several important approaches: wind power integration and carbon reduction cooperation, it is necessary to deal with the uncertainties of wind power and carbon reduction modes for thermal power encountered in low-carbon power dispatch. For this purpose, this paper firstly presents a distributed robust optimization model synthetically considering robustness, economy, and environment. Next, wind power characterizations, scenario division and compression methods, and allocation algorithms of initial carbon emission rights are fully discussed for the convenience of model solution. Finally, empirical analysis shows that (1) the proposed model proves to be effective not only in coping with wind power uncertainties and reducing operating costs, (2) but also in dealing with the uncertainties of carbon reduction modes and reducing carbon emissions, and (3) low-carbon power dispatching strategies combining robustness, economy, and environment could be achieved through the proposed model and method, which are especially helpful to minimize interference from these two types of uncertainty more scientifically and reasonably.

Predicting the trend of infectious diseases using grey self-memory system model: a case study of the incidence of tuberculosis.

OBJECTIVES: The prediction and early warning of infectious diseases is an important work in the field of public health. This study constructed the grey self-memory system model to predict the incidence trend of infectious diseases affected by many uncertain factors. STUDY DESIGN: The design of this study is a combination of the prediction method and empirical analysis. METHODS: By organically coupling the self-memory algorithm with the mean GM(1,1) model, the tuberculosis incidence statistics of China from 2004 to 2018 were selected for prediction analysis. Meanwhile, by comparing with the other traditional prediction methods, three representative accuracy check indexes (MSE, AME, MAPE) were conducting for error analysis. RESULTS: Owing to the multiple time-points initial fields, which replace the single time-points, the limitation of the traditional grey prediction model, which is sensitive to the initial value, is overcome in the self-memory equation. Consequently, compared with the mean GM model and other statistical methods, the grey self-memory model shows significant forecasting advantages, and its single-step rolling prediction accuracy is superior to other prediction methods. Therefore, the incidence of tuberculosis in China in the next year can be predicted as 55.30 (unit: 1/105). CONCLUSIONS: The grey self-memory system model can closely capture the individual random fluctuation in the whole evolution trend of the uncertain system. It is appropriate for predicting the future incidence trend of infectious diseases and is worth popularizing to other similar public health prediction problems.

[Characteristics of non-point source pollution in Tiaoxi watershed and related affecting factors].

By using soil and water assessment tool (SWAT) model, this paper simulated the surface runoff intensity and the export loadings of sediment particulates and nutrients via non-point source hydrological pathway in Tiaoxi watershed, and integrated with the simulation results, analyzed the temporal and spatial distribution characteristics of non-point source pollution in the watershed in 2008. In the study area, the per unit area non-point source pollution was stronger in northern region than in southern region and in eastern region than in western region, and the weakest in central region. Among the land utilization types, farmland had the biggest contribution to the sediment loading. There were significantly positive correlations between the loadings of surface runoff and associated sediment particulates and the rainfall intensity. The export loadings of nutrients through surface runoff were higher in rainy season (from June to September) than in dry season (from December to next March), and there existed significant correlations between the surface runoff loadings of sediment particulates, organic nitrogen, and nitrate and the average gradient of lands.