Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the applica...Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-researched.In this study,stepwise multiple linear regression(SMLR)and Random Forest(RF)were used to evaluate the spatial and temporal variation of NUE indicators(i.e.,partial factor productivity of N(PFPN);partial nutrient balance of N(PNBN))at county scale in Northeast China(Heilongjiang,Liaoning and Jilin provinces)from 1990 to 2015.Explanatory variables included agricultural management practices,topography,climate,economy,soil and crop types.Results revealed that the PFPN was higher in the northern parts and lower in the center of the Northeast China and PNBN increased from southern to northern parts during the 1990–2015 period.The NUE indicators decreased with time in most counties during the study period.The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFPN,and 0.67 and 0.89 for PNBN,respectively.The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model.The planting area index of vegetables and beans,soil clay content,saturated water content,enhanced vegetation index in November&December,soil bulk density,and annual minimum temperature were the main explanatory variables for both NUE indicators.This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR.This novel study gives reference measurements to improve crop NUE which is one of the most effective means of managing N for sustainable development,ensuring food security,alleviating environmental degradation and increasing farmer’s profitability.展开更多
Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to p...Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and elastic modulus(E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets.Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of KIC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination(R2),root mean square error(RMSE), and mean absolute error(MAE) were utilized herein. In the case of testing datasets, the values of R2, RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156,respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R2value and lower errors.展开更多
Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have dev...Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.展开更多
Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to d...Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.展开更多
As an effort to understand the effect of diabetes on the increasing rate of COVID-19 infection, we embarked upon a detailed statistical analysis of various datasets that include COVID-19 infection and mortality rate, ...As an effort to understand the effect of diabetes on the increasing rate of COVID-19 infection, we embarked upon a detailed statistical analysis of various datasets that include COVID-19 infection and mortality rate, diabetes and diseases that may contribute to the severity and risk factor of diabetes in individuals and this impact on COVID-19 and the mortality rate. These diseases include respiratory diseases, cardiovascular diseases, and obesity. Equally significant is the statistical analysis on ethnicity, age, and sex on COVID-19 infection as well as mortality rate. Their possible contributions to increasing the severity and risk factor of diabetes as a risk to mortality to individuals who have COVID-19. Objectives: The ultimate objectives of this investigation are as follow: 1) Is there a risk factor of diabetes on COVID-19 infection and increasing mortality rate? 2) To what extent do other disease conditions that include, obesity, heart failure, and respiratory diseases influence the severity and risk factor of diabetes on increasing COVID-19 infection and mortality rate? 3) To what extent does age, race, and gender increase the mortality of COVID-19 and increase the severity and risk factor of diabetes on COVID-19 mortality rate? 4) How and why COVID-19 virus increases the risk of diabetes in children? 5) Diabetes and COVID-19: Who is most at Risk? Lastly, understanding the misconception of COVID-19 and diabetes.展开更多
[目的/意义]将以某个国家为研究对象的科学研究过程称为对该国的学术关注,并从国家层面开展学术关注度量与影响因素分析;旨在从宏观上了解国家学术关注行为的影响因素,并为理解国际关系提供新视角。[方法/过程]基于Web of Scicence核心...[目的/意义]将以某个国家为研究对象的科学研究过程称为对该国的学术关注,并从国家层面开展学术关注度量与影响因素分析;旨在从宏观上了解国家学术关注行为的影响因素,并为理解国际关系提供新视角。[方法/过程]基于Web of Scicence核心合集数据库,采集1998―2021年世界各国有关其他国家研究的论文数据,开展国家学术关注的度量和描述性统计分析;并从主客体国家特征和国家联系两个维度,采用多重线性回归方法,探索国家学术关注行为的影响因素。[局限]由于国家学术关注的影响因素非常复杂,不少因素存在难以获取和难以量化的问题。对于国家学术关注的影响因素的回归分析不够精确,有待进一步优化。[结果/结论]从散点图看,一个国家对于全球的学术关注程度和受到全球的学术关注量均受到该国的地理因素、社会因素、经济因素、科技因素、军事因素的影响。通过回归分析发现,在其它因素不变的情况下,军费开支大、国土面积大、人口总数大的国家受到全球的学术关注量更大,人口年度增长率高的国家对于全球的学术关注程度更大;部分情况下国土面积小、科技论文发文量小的国家对于全球的学术关注程度更高。与中国之间的自然、文化和经济联系均显著影响中国对其他国家的学术关注,在其他因素不变的情况下,与中国具有共同官方语言、国土接壤、进出口贸易额越大的国家较全球平均水平受到中国的学术关注更大。以上各种因素的影响随着国家范围和时间的变化而变化。展开更多
基金the China Scholarship Council(CSC)(201903250115)the National Natural Science Foundation of China(31972515)the China Agriculture Research System of MOF and MARA(CARS-09-P31).
文摘Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-researched.In this study,stepwise multiple linear regression(SMLR)and Random Forest(RF)were used to evaluate the spatial and temporal variation of NUE indicators(i.e.,partial factor productivity of N(PFPN);partial nutrient balance of N(PNBN))at county scale in Northeast China(Heilongjiang,Liaoning and Jilin provinces)from 1990 to 2015.Explanatory variables included agricultural management practices,topography,climate,economy,soil and crop types.Results revealed that the PFPN was higher in the northern parts and lower in the center of the Northeast China and PNBN increased from southern to northern parts during the 1990–2015 period.The NUE indicators decreased with time in most counties during the study period.The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFPN,and 0.67 and 0.89 for PNBN,respectively.The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model.The planting area index of vegetables and beans,soil clay content,saturated water content,enhanced vegetation index in November&December,soil bulk density,and annual minimum temperature were the main explanatory variables for both NUE indicators.This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR.This novel study gives reference measurements to improve crop NUE which is one of the most effective means of managing N for sustainable development,ensuring food security,alleviating environmental degradation and increasing farmer’s profitability.
文摘Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and elastic modulus(E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets.Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of KIC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination(R2),root mean square error(RMSE), and mean absolute error(MAE) were utilized herein. In the case of testing datasets, the values of R2, RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156,respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R2value and lower errors.
基金This research was funded by the National Natural Science Foundation of China(grant no.32271881).
文摘Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.
文摘Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.
文摘As an effort to understand the effect of diabetes on the increasing rate of COVID-19 infection, we embarked upon a detailed statistical analysis of various datasets that include COVID-19 infection and mortality rate, diabetes and diseases that may contribute to the severity and risk factor of diabetes in individuals and this impact on COVID-19 and the mortality rate. These diseases include respiratory diseases, cardiovascular diseases, and obesity. Equally significant is the statistical analysis on ethnicity, age, and sex on COVID-19 infection as well as mortality rate. Their possible contributions to increasing the severity and risk factor of diabetes as a risk to mortality to individuals who have COVID-19. Objectives: The ultimate objectives of this investigation are as follow: 1) Is there a risk factor of diabetes on COVID-19 infection and increasing mortality rate? 2) To what extent do other disease conditions that include, obesity, heart failure, and respiratory diseases influence the severity and risk factor of diabetes on increasing COVID-19 infection and mortality rate? 3) To what extent does age, race, and gender increase the mortality of COVID-19 and increase the severity and risk factor of diabetes on COVID-19 mortality rate? 4) How and why COVID-19 virus increases the risk of diabetes in children? 5) Diabetes and COVID-19: Who is most at Risk? Lastly, understanding the misconception of COVID-19 and diabetes.
文摘[目的/意义]将以某个国家为研究对象的科学研究过程称为对该国的学术关注,并从国家层面开展学术关注度量与影响因素分析;旨在从宏观上了解国家学术关注行为的影响因素,并为理解国际关系提供新视角。[方法/过程]基于Web of Scicence核心合集数据库,采集1998―2021年世界各国有关其他国家研究的论文数据,开展国家学术关注的度量和描述性统计分析;并从主客体国家特征和国家联系两个维度,采用多重线性回归方法,探索国家学术关注行为的影响因素。[局限]由于国家学术关注的影响因素非常复杂,不少因素存在难以获取和难以量化的问题。对于国家学术关注的影响因素的回归分析不够精确,有待进一步优化。[结果/结论]从散点图看,一个国家对于全球的学术关注程度和受到全球的学术关注量均受到该国的地理因素、社会因素、经济因素、科技因素、军事因素的影响。通过回归分析发现,在其它因素不变的情况下,军费开支大、国土面积大、人口总数大的国家受到全球的学术关注量更大,人口年度增长率高的国家对于全球的学术关注程度更大;部分情况下国土面积小、科技论文发文量小的国家对于全球的学术关注程度更高。与中国之间的自然、文化和经济联系均显著影响中国对其他国家的学术关注,在其他因素不变的情况下,与中国具有共同官方语言、国土接壤、进出口贸易额越大的国家较全球平均水平受到中国的学术关注更大。以上各种因素的影响随着国家范围和时间的变化而变化。
