Grey System GM Research Articles

Using Grey System GM(1,1) Model to Predict the Drug-GPCRs Couples

It is an important topic to find and identify molecular drug targets for modern drug research. Naturally, to find new or potential drug-GPCRs couples is very useful for determining the new effective medicine target, because GPCRs are among the most frequent targets of therapeutic drugs and proven evolutionary pharmacology value. To realize this, this paper incorporates chemical functional groups of drugs and grey model GM(1,1) based on digital coding of amino acids to formulate the drug-GPCRs couples for statistical prediction. The overall success rate using the fuzzy K-Nearest Neighbor algorithm by the jackknife test is about 81%. This novel approach will further stimulate the development of scanning target via a computational approach.

Combination GM(1,1) and Stochastic Poroelastic Theory to Prediction the Land Subsidence in Yunlin and Jiayi Area, Taiwan

In this study, the grey system GM(1,1) and the geostatistical method are initially used to predict the land subsidence of the Yunlin area in the next five years. The Yunlin County Yuanchang Element School is taken as the site example when the stochastic poroelastic model is used in the study because there is a lack of data related to land subsidence. The verification results and prediction data of the stochastic poroelastic model and the grey system are displayed. The results show that the land subsidence in the next five years as calculated based on the data obtained from the monitoring wells is 0.3 m, while 0.31 m and 0.33 m are the prediction results from the stochastic poroelastic model and grey system model, respectively. This indicates the high precision of both models in predicting land subsidence. In order to simulate the effect of climate change on territorial planning, a prediction is made on the possible land subsidence for 2030, in this study. The prediction results are shows that by 25 January 2030, the stochastic poroelastic model shows a land subsidence of 1.01 m, while it is 1.68 m for the Grey System model. Because only the Changyuan Element School is taken as an observation station for the stochastic poroelastic model, the Grey System model is used to predict land subsidence for 2007 by the geostatistical method. The results show that land subsidence will mainly occur in the mid-western area of Yunlin and the western coastal area of Jianyi. To simulate the possible situations in the year 2030, four scene simulation models are proposed in this research, that is, adding 10% discharge, adding 20% discharge, subtracting 10% discharge, and subtracting 20% discharge. The results show the prediction on land subsidence for the year 2003 when add 10% discharge, subtract 10% discharge, add 20% discharge, and subtract 20% discharge, respectively. Land subsidence will occur mainly in the mid-western area of Yunlin and the western coastal area of Jiayi. The maximum land subsidence could reach up to 150 cm or so.

Karst Hydrological Processes and Grey System Model1

Hao, Yonghong, Jiaojuan Zhao, Huamin Li, Bibo Cao, Zhongtang Li, and Tian‐Chyi J. Yeh, 2012. Karst Hydrological Processes and Grey System Model. Journal of the American Water Resources Association (JAWRA) 48(4): 656‐666. DOI: 10.1111/j.1752‐1688.2012.00640.xAbstract: The karst hydrological processes are the response of karst groundwater system to precipitation. This study provided a concept model of karst hydrological processes. The hydraulic response time of spring discharge to precipitation includes the time that precipitation penetrates through the vadose zone, and the subsequent groundwater pressure wave propagates to a spring outlet. Due to heterogeneities in karst aquifers, the hydraulic response time is different in different areas. By using grey system theory, we proposed a karst hydrological model that offers a calculation of hydraulic response time, and a response model of spring discharge to precipitation. Then, we applied the models to the Liulin Springs Basin, China. In the south part of the Liulin Springs Basin, where large fields of carbonate rocks outcrop with intensive karstification, the hydraulic response time is one year. In the north, where the Ordovician karst aquifer is covered by Quaternary loess sediments, the response time is seven years. The grey system GM(1,3) response model of spring discharge to precipitation was applied in consideration of the hydraulic response time. The model calibration showed that the average error was 6.55%, and validation showed that the average error was 12.19%.

Application of Time Series Equal-space GM (1, 1) on Slope Displacement Prediction

Abstract: Slop disasters, which are blurry and uncertainly, are influenced by random variables. So it is preponderant to use the gray theory to predict the displacement of slopes. The traditional model of equal interval gray system-GM (1, 1) needs equal interval monitoring data to get prediction, but most of time, we can only get unequal interval monitoring data. Therefore put forward a method that firstly make the unequal interval monitoring data equal-space and then use GM (1, 1) to forecast the displacement. Use Matlab to design corresponding program of this model, prove that this model is hightly accurate and can be applied to practical engineering with an example.

Grey system model with time lag and application to simulation of karst spring discharge

PurposeThe purpose of this paper is to introduce the concept of time lag to grey system models, and propose the grey system models with time lags.Design/methodology/approachThe models were applied to calculate the time lags between spring flow and precipitation (i.e. hydraulic response time) and simulate karst spring discharge in Liulin Springs Basin, China.FindingsThe results show that the time lags of Lishi, Zhongyang, and Fangshan are three, five, and eight, respectively. By considering the time lags, we set up the grey system GM(1,4) response model of Liulin Springs discharge to precipitation of Lishi, Zhongyang, and Fangshan.Originality/valueThe results are in agreement with regional geological structure and provide a better understanding of karst hydrological processes of Liulin Springs Basin.

Energy Consumption Combination Forecast of Hebei Province Based on the IOWA Operator

Abstract Energy is the essential material for human survival, and it is the important resource for economic development and social progress. Making a reasonable and reliable development plan by forecasting the energy consumption scientifically and effectively can ensure economic healthily and orderly developing. The combination forecast method based on inducted ordered weighted average operator is used widely in these years. The difference between this model and the traditional combination forecast method is that the empowerment coefficient of the combination forecast has nothing to do with the monomial prediction model, but it is closely related to forecast precision's size of the monomial prediction model at each time-point, it is an invariable empowerment method. In this paper we adopt the time series tendency forecast model and the gray system GM (1, 1) forecast model in order to analysis the energy consumption of the Hebei Province; then, it introduces the energy consumption combination forecast model based on the IOWA operator. Empirical analysis indicates that this model can enhance the forecast precision of the combination and it is an effective combination forecast method.

Study on investment scale control of urban construction process by the method of grey systems

Purpose – The purpose of this paper is to study the theory of urban investment. Urban construction investment should be optimized. The situation of the investment funds operation should be controlled, and the biggest total benefit in the whole town construction activity will be produced. Since some valid measure is taken, the amount of the plan value will be controlled.Design/methodology/approach – Against the problem of low efficiency, wasting and losing control of urban construction investment, research by the method of mathematics on the investment of urban construction is done. The standpoint is putting forward of the investment control of the whole process of urban construction. The Gray system GM(1, 1) wrap model is adopted to seek the feasible scale of the investment.Findings – The Gray system GM(1, 1) wrap model is adopted to seek the feasible scale of the investment. The crisis management is taken in the process, and the three‐step theory of sequential control is put forward.Research limitations/...

Forecast of flood in Chaohu Lake basin of China based on Grey-Markov theory

Flood is one kind of unexpected and the most common natural disasters, which is affected by many factors and has complex mechanism. At home and abroad, there is still no mature theory and method used for the long-term forecast of natural precipitation at present. In the present paper the disadvantages of grey GM (1, 1) and Markov chain are ana- lyzed, and Grey-Markov forecast theory about flood is put forward and then the modifying model is developed by making prediction of Chaohu Lake basin. Hydrological law was conducted based on the theoretical forecasts by grey system GM (1, 1) forecast model with improved Markov chain. The above method contained Stat-analysis, embodying scientific approach, precise forecast and its reliable results.

Study on the Application of the Combined Prediction Modeling Method to Thermal Error Modeling on NC Machine Tools

Due to the complexity of machine tool thermal errors affected by various factors, a new combining prediction model, based on the theory of gery system GM (1,1) model, is applied to the trend prediction of machine tool thermal errors. The degree of smoothness of primary data sequence is first improved by function transform method and sequentially grey system GM (1,1) model is established; second, time series analysis model is established by remnant sequence of GM (1,1) model to amend the precision of grey system GM (1,1) model. Thus, the precision of combining prediction model is further improved. Through the prediction study on thermal error modeling in a spot NC turning center, testing results showed that combining prediction model can highly improve machine tool’s prediction precision and make it more effective for real-time compensation of machine tool thermal error.

A gray system model for studying the response to climatic change: The Liulin karst springs, China

Gray system theory uses a black-gray-white color spectrum to describe a complex system whose characteristics are only partially known or known with uncertainty. In this study, we use gray system theory to investigate the relation between precipitation and spring flows in a karst region in China. The gray incidence analysis was applied to the Liulin Springs, Shanxi Province, China to analyze the time-lag between spring flow and precipitation. The results showed that the average groundwater residence time at Liulin Spings is about 4 years. The gray system GM(1,2) model was subsequently used as a predictive tool for spring discharge. It was found that model predictions are in agreement with observed data. This study also shows that the discharge of the Liulin Springs primarily responds to climate change; anthropogenic impacts are secondary. The continuous decline of water level in the karst aquifer and waning of spring discharges in semi-arid regions of China might be largely a response of the groundwater system to the decline in regional precipitation over the past two decades.

The morbidity and mortality trend and prediction of lung cancer in residents of Nangang District of Harbin in China during the past 10 years

It is not clear yet about the secular changes of morbidity and mortality trend of lung cancer in residents of Nangang District of Harbin in China. The aim of this study is to estimate the trend of lung cancer morbidity and mortality in residents of Nangang District from 1992 to 2001 and to predict their levels in the future 5 years. Data were collected from the annual statistic reports on cancer death cause from Nangang District in Harbin. The classification of death cause was made according to the ICD-9. Predictions about morbidity and mortality were made by the gray system GM(1,1). During the past 10 years, the morbidity and mortality of lung cancer were placed in uptrend slowly. The average morbidity and mortality of lung cancer were 44.75 per 100000 and 41.37 per 100000 respectively, and lung cancer was the first leading cancer for both episode and death of malignant tumors. The proportions of lung cancer were 25.91% and 33.29% for episode and death in all malignant tumors respectively. A half patients with lung cancer was 20-64 years old. Predictive morbidity and mortality of lung cancer would be 47.79/100000 and 44.81/100000 for men and 45.80/100000 and 42.02/100000 for women respectively. The morbidity and mortality of lung cancer show a slowly increasing trend. Lung cancer is one of main malignant tumors among people of 20-64 years old. The gradually aging population, environmental pollution and individual unhealthy living habits are the important factors of lung cancer increasing.