The purpose of this study was to examine the burnout levels of research assistants in Ondokuz Mayis University and to examine the results of multiple linear regression model based on the results obtained from Maslach ...The purpose of this study was to examine the burnout levels of research assistants in Ondokuz Mayis University and to examine the results of multiple linear regression model based on the results obtained from Maslach Burnout Scale with Jackknife Method in terms of validity and generalizability. To do this, a questionnaire was given to 11 research assistants working at Ondokuz Mayis University and the burnout scores of this questionnaire were taken as the dependent variable of the multiple linear regression model. The variable of burnout was explained with the variables of age, weekly hours of classes taught, monthly average credit card debt, numbers of published articles and reports, gender, marital status, number of children and the departments of the research assistants. Dummy variables were assigned to the variables of gender, marital status, number of children and the departments of the research assistants and thus, they were made quantitative. The significance of the model as a result of multiple linear regressions was examined through backward elimination method. After this, for the five explanatory variables which influenced the variable of burnout, standardized model coefficients and coefficients of determination, and 95% confidence intervals of these values were estimated through Jackknife Method and the generalizability of the parameter estimation results of these variables on population was researched.展开更多
In recent years, aluminum-matrix composites (AMCs) have been widely used to replace cast iron in aerospace and automotive industries. Machining of these composite materials requires better understanding of cutting pro...In recent years, aluminum-matrix composites (AMCs) have been widely used to replace cast iron in aerospace and automotive industries. Machining of these composite materials requires better understanding of cutting processes re- garding accuracy and efficiency. This study addresses the modeling of the machinability of self-lubricated aluminum /alumina/graphite hybrid composites synthesized by the powder metallurgy method. In this study, multiple regression analysis (MRA) and artificial neural networks (ANN) were used to investigate the influence of some parameters on the thrust force and torque in the drilling processes of self-lubricated hybrid composite materials. The models were identi- fied by using cutting speed, feed, and volume fraction of the reinforcement particles as input data and the thrust force and torque as the output data. A comparison between two prediction methods was developed to compare the prediction accuracy. ANNs showed better predictability results compared to MRA due to the nonlinearity nature of ANNs. The statistical analysis accompanied with artificial neural network results showed that Al2O3, Gr and cutting feed (f) were the most significant parameters on the drilling process, while spindle speed seemed insignificant. Since the spindle speed was insignificant, it directed us to set it either at the highest spindle speed to obtain high material removal rate or at the lowest spindle speed to prolong the tool life depending on the need for the application.展开更多
Due to the heterogeneity of rock masses and the variability of in situ stress,the traditional linear inversion method is insufficiently accurate to achieve high accuracy of the in situ stress field.To address this cha...Due to the heterogeneity of rock masses and the variability of in situ stress,the traditional linear inversion method is insufficiently accurate to achieve high accuracy of the in situ stress field.To address this challenge,nonlinear stress boundaries for a numerical model are determined through regression analysis of a series of nonlinear coefficient matrices,which are derived from the bubbling method.Considering the randomness and flexibility of the bubbling method,a parametric study is conducted to determine recommended ranges for these parameters,including the standard deviation(σb)of bubble radii,the non-uniform coefficient matrix number(λ)for nonlinear stress boundaries,and the number(m)and positions of in situ stress measurement points.A model case study provides a reference for the selection of these parameters.Additionally,when the nonlinear in situ stress inversion method is employed,stress distortion inevitably occurs near model boundaries,aligning with the Saint Venant's principle.Two strategies are proposed accordingly:employing a systematic reduction of nonlinear coefficients to achieve high inversion accuracy while minimizing significant stress distortion,and excluding regions with severe stress distortion near the model edges while utilizing the central part of the model for subsequent simulations.These two strategies have been successfully implemented in the nonlinear in situ stress inversion of the Xincheng Gold Mine and have achieved higher inversion accuracy than the linear method.Specifically,the linear and nonlinear inversion methods yield root mean square errors(RMSE)of 4.15 and 3.2,and inversion relative errors(δAve)of 22.08%and 17.55%,respectively.Therefore,the nonlinear inversion method outperforms the traditional multiple linear regression method,even in the presence of a systematic reduction in the nonlinear stress boundaries.展开更多
In order to solve the problem of chronic heart failure risk prediction in the elderly,a logistic regression modeling framework with Bayesian method was proposed,aiming to solve the problem of insufficient generalizati...In order to solve the problem of chronic heart failure risk prediction in the elderly,a logistic regression modeling framework with Bayesian method was proposed,aiming to solve the problem of insufficient generalization perfor-mance caused by overfitting in small sample data of traditional logistic regres-sion.By including 16 multi-dimensional clinical indicators(age,gender,BMI and alcohol history,etc.)in 20 elderly patients with chronic heart failure,the initial feature set was multicollinearity screened based on the variance infla-tion factor(VIF)test,and the high collinearity variables with VIF value≥10(such as fall risk,frailty assessment,etc.)were retained,so as to reduce the interference of redundant information on the stability of the model.Subse-quently,the entropy weight method was used to weight the filtered variables,and the information contribution of each index was quantified by information entropy,and standardized weighted data was generated,so as to optimize the feature importance allocation and alleviate the residual collinearity.Finally,based on the weighted data,Spearman correlation analysis was used to quan-titatively evaluate the association strength of each variable with heart failure classification,and the core predictors of balance and gait ability(correlation coefficient 0.52)and physical function status were identified.The results show that although the traditional logistic model achieves 100%accuracy on the training set,its parameters are significantly abnormal due to the singularity of the Hasten matrix,indicating that the model has a serious risk of overfitting.To this end,a Bayesian framework was introduced in this study,with a normal prior constraint regression coefficient with a mean of 0 and a standard devia-tion of 10,through the Markov Chain Monte Carlo(MCMC).The posterior distribution of parameters is obtained by sampling,which effectively balances the complexity of the model and the likelihood of the data.The experimental results show that Bayesian logistic regression has a classification accuracy of 85%on the independent test set,and the confusion matrix shows that the mis-judgments are only concentrated in the categories with overlapping features(one case in the second category is misjudged to the first category),and the F1 score is significantly improved(category 1:0.86,category 2:0.80,category 3:1.00),which avoids the singularity of the Haysen matrix.This study confirms that Bayesian logistic regression provides a highly robust solution for model-ing chronic heart failure in small elderly populations through probability reg-ularization and uncertainty quantification.展开更多
In recent years,the ecological environment along highways in Tibetan Plateau has been severely affected due to the rapid construction of highways.In order to solve the problems of multiple indicators and inconsistent ...In recent years,the ecological environment along highways in Tibetan Plateau has been severely affected due to the rapid construction of highways.In order to solve the problems of multiple indicators and inconsistent criteria in the ecological impact assessment of highways,and to scientifically screen assessment indicators,the paper proposes a multi-round indicator screening method,which combines literature analysis,expert rating,and statistical analysis.Based on this screening method,normalized difference vegetation index,land surface temperature,elevation,and normalized difference soil index are screened out.Combined with multiple linear regression,an ecological impact assessment model is established and applied to ecological impact assessment of Gonghe-Yushu Expressway.The results show that the expressway construction is the first driving force for the deterioration of the ecological environment along the roadside,and its interference range on the desert grassland ecosystem is greater than that on the agroforestry system.The ecological environment within 150 m on both sides of the expressway should be protected.展开更多
For Microwave Humidity and Temperature sounder(MWHTS) measurements over the ocean, a cloud filtering method is presented to filter out cloud-and precipitation-affected observations by analyzing the sensitivity of the ...For Microwave Humidity and Temperature sounder(MWHTS) measurements over the ocean, a cloud filtering method is presented to filter out cloud-and precipitation-affected observations by analyzing the sensitivity of the simulated brightness temperatures of MWHTS to cloud liquid water, and using the root mean square error(RMSE)between observation and simulation in clear sky as a reference standard. The atmospheric temperature and humidity profiles are retrieved using MWHTS measurements with and without filtering by multiple linear regression(MLR),artificial neural networks(ANN) and one-dimensional variational(1DVAR) retrieval methods, respectively, and the effects of the filtering method on the retrieval accuracies are analyzed. The numerical results show that the filtering method can improve the retrieval accuracies of the MLR and the 1DVAR retrieval methods, but have little influence on that of the ANN. In addition, the dependencies of the retrieval methods upon the testing samples of brightness temperature are studied, and the results show that the 1DVAR retrieval method has great stability due to that the testing samples have great impact on the retrieval accuracies of the MLR and the ANN, but have little impact on that of the 1DVAR.展开更多
为优化地下水水质监测方案,确定北京顺平岩溶水系统岩溶水水质的主要控制指标,快速掌握地下水水质变化,基于2022年顺平岩溶水系统49件岩溶水样品,综合运用统计分析、Piper三线图和熵权水质指数(entropy weight water quality index,EWQI...为优化地下水水质监测方案,确定北京顺平岩溶水系统岩溶水水质的主要控制指标,快速掌握地下水水质变化,基于2022年顺平岩溶水系统49件岩溶水样品,综合运用统计分析、Piper三线图和熵权水质指数(entropy weight water quality index,EWQI)分析研究区水化学和水质特征,并耦合逐步多元线性回归分析,探讨能代表研究区岩溶水水质的关键指标。结果显示:①顺平岩溶水系统岩溶水具有微碱性、低盐度的特征,水化学类型主要为HCO_(3)^(-)—Ca^(2+)·Mg^(2+)型(73.47%)。超标指标为N_(an)(NH_(3)或NH_(4)^(+)中的N,即氨氮以氮计)、pH值、Fe、Mn和F^(-),超标率分别为10.20%、4.08%、4.08%、4.08%和2.04%。②研究区EWQI平均值为26.33,水质“极好”,其中极好和良好所占比例分别为91.84%和8.16%。③基于地下水水质数据构建的EWQI_(min)模型筛选的关键指标为N_(an)、Fe、Mn、N_(ntr)(硝氮以氮计,硝酸盐中N)和F^(-),其决定系数(R^(2))和百分比误差(PE)分别为0.986和3.88%。表明,EWQI_(min)模型优选指标可以代表顺平岩溶水系统的水质状况,对优化水质监测网等水资源管理提供了参考价值。展开更多
文摘The purpose of this study was to examine the burnout levels of research assistants in Ondokuz Mayis University and to examine the results of multiple linear regression model based on the results obtained from Maslach Burnout Scale with Jackknife Method in terms of validity and generalizability. To do this, a questionnaire was given to 11 research assistants working at Ondokuz Mayis University and the burnout scores of this questionnaire were taken as the dependent variable of the multiple linear regression model. The variable of burnout was explained with the variables of age, weekly hours of classes taught, monthly average credit card debt, numbers of published articles and reports, gender, marital status, number of children and the departments of the research assistants. Dummy variables were assigned to the variables of gender, marital status, number of children and the departments of the research assistants and thus, they were made quantitative. The significance of the model as a result of multiple linear regressions was examined through backward elimination method. After this, for the five explanatory variables which influenced the variable of burnout, standardized model coefficients and coefficients of determination, and 95% confidence intervals of these values were estimated through Jackknife Method and the generalizability of the parameter estimation results of these variables on population was researched.
文摘In recent years, aluminum-matrix composites (AMCs) have been widely used to replace cast iron in aerospace and automotive industries. Machining of these composite materials requires better understanding of cutting processes re- garding accuracy and efficiency. This study addresses the modeling of the machinability of self-lubricated aluminum /alumina/graphite hybrid composites synthesized by the powder metallurgy method. In this study, multiple regression analysis (MRA) and artificial neural networks (ANN) were used to investigate the influence of some parameters on the thrust force and torque in the drilling processes of self-lubricated hybrid composite materials. The models were identi- fied by using cutting speed, feed, and volume fraction of the reinforcement particles as input data and the thrust force and torque as the output data. A comparison between two prediction methods was developed to compare the prediction accuracy. ANNs showed better predictability results compared to MRA due to the nonlinearity nature of ANNs. The statistical analysis accompanied with artificial neural network results showed that Al2O3, Gr and cutting feed (f) were the most significant parameters on the drilling process, while spindle speed seemed insignificant. Since the spindle speed was insignificant, it directed us to set it either at the highest spindle speed to obtain high material removal rate or at the lowest spindle speed to prolong the tool life depending on the need for the application.
基金funded by the National Key R&D Program of China(Grant No.2022YFC2903904)the National Natural Science Foundation of China(Grant Nos.51904057 and U1906208).
文摘Due to the heterogeneity of rock masses and the variability of in situ stress,the traditional linear inversion method is insufficiently accurate to achieve high accuracy of the in situ stress field.To address this challenge,nonlinear stress boundaries for a numerical model are determined through regression analysis of a series of nonlinear coefficient matrices,which are derived from the bubbling method.Considering the randomness and flexibility of the bubbling method,a parametric study is conducted to determine recommended ranges for these parameters,including the standard deviation(σb)of bubble radii,the non-uniform coefficient matrix number(λ)for nonlinear stress boundaries,and the number(m)and positions of in situ stress measurement points.A model case study provides a reference for the selection of these parameters.Additionally,when the nonlinear in situ stress inversion method is employed,stress distortion inevitably occurs near model boundaries,aligning with the Saint Venant's principle.Two strategies are proposed accordingly:employing a systematic reduction of nonlinear coefficients to achieve high inversion accuracy while minimizing significant stress distortion,and excluding regions with severe stress distortion near the model edges while utilizing the central part of the model for subsequent simulations.These two strategies have been successfully implemented in the nonlinear in situ stress inversion of the Xincheng Gold Mine and have achieved higher inversion accuracy than the linear method.Specifically,the linear and nonlinear inversion methods yield root mean square errors(RMSE)of 4.15 and 3.2,and inversion relative errors(δAve)of 22.08%and 17.55%,respectively.Therefore,the nonlinear inversion method outperforms the traditional multiple linear regression method,even in the presence of a systematic reduction in the nonlinear stress boundaries.
文摘In order to solve the problem of chronic heart failure risk prediction in the elderly,a logistic regression modeling framework with Bayesian method was proposed,aiming to solve the problem of insufficient generalization perfor-mance caused by overfitting in small sample data of traditional logistic regres-sion.By including 16 multi-dimensional clinical indicators(age,gender,BMI and alcohol history,etc.)in 20 elderly patients with chronic heart failure,the initial feature set was multicollinearity screened based on the variance infla-tion factor(VIF)test,and the high collinearity variables with VIF value≥10(such as fall risk,frailty assessment,etc.)were retained,so as to reduce the interference of redundant information on the stability of the model.Subse-quently,the entropy weight method was used to weight the filtered variables,and the information contribution of each index was quantified by information entropy,and standardized weighted data was generated,so as to optimize the feature importance allocation and alleviate the residual collinearity.Finally,based on the weighted data,Spearman correlation analysis was used to quan-titatively evaluate the association strength of each variable with heart failure classification,and the core predictors of balance and gait ability(correlation coefficient 0.52)and physical function status were identified.The results show that although the traditional logistic model achieves 100%accuracy on the training set,its parameters are significantly abnormal due to the singularity of the Hasten matrix,indicating that the model has a serious risk of overfitting.To this end,a Bayesian framework was introduced in this study,with a normal prior constraint regression coefficient with a mean of 0 and a standard devia-tion of 10,through the Markov Chain Monte Carlo(MCMC).The posterior distribution of parameters is obtained by sampling,which effectively balances the complexity of the model and the likelihood of the data.The experimental results show that Bayesian logistic regression has a classification accuracy of 85%on the independent test set,and the confusion matrix shows that the mis-judgments are only concentrated in the categories with overlapping features(one case in the second category is misjudged to the first category),and the F1 score is significantly improved(category 1:0.86,category 2:0.80,category 3:1.00),which avoids the singularity of the Haysen matrix.This study confirms that Bayesian logistic regression provides a highly robust solution for model-ing chronic heart failure in small elderly populations through probability reg-ularization and uncertainty quantification.
基金supported by the National Natural Science Foundation of China(NSFC)(No.41801387)。
文摘In recent years,the ecological environment along highways in Tibetan Plateau has been severely affected due to the rapid construction of highways.In order to solve the problems of multiple indicators and inconsistent criteria in the ecological impact assessment of highways,and to scientifically screen assessment indicators,the paper proposes a multi-round indicator screening method,which combines literature analysis,expert rating,and statistical analysis.Based on this screening method,normalized difference vegetation index,land surface temperature,elevation,and normalized difference soil index are screened out.Combined with multiple linear regression,an ecological impact assessment model is established and applied to ecological impact assessment of Gonghe-Yushu Expressway.The results show that the expressway construction is the first driving force for the deterioration of the ecological environment along the roadside,and its interference range on the desert grassland ecosystem is greater than that on the agroforestry system.The ecological environment within 150 m on both sides of the expressway should be protected.
基金Key Fostering Project of National Space Science Center,Chinese Academy of Sciences(Y62112f37s)National 863 Project of China(2015AA8126027)
文摘For Microwave Humidity and Temperature sounder(MWHTS) measurements over the ocean, a cloud filtering method is presented to filter out cloud-and precipitation-affected observations by analyzing the sensitivity of the simulated brightness temperatures of MWHTS to cloud liquid water, and using the root mean square error(RMSE)between observation and simulation in clear sky as a reference standard. The atmospheric temperature and humidity profiles are retrieved using MWHTS measurements with and without filtering by multiple linear regression(MLR),artificial neural networks(ANN) and one-dimensional variational(1DVAR) retrieval methods, respectively, and the effects of the filtering method on the retrieval accuracies are analyzed. The numerical results show that the filtering method can improve the retrieval accuracies of the MLR and the 1DVAR retrieval methods, but have little influence on that of the ANN. In addition, the dependencies of the retrieval methods upon the testing samples of brightness temperature are studied, and the results show that the 1DVAR retrieval method has great stability due to that the testing samples have great impact on the retrieval accuracies of the MLR and the ANN, but have little impact on that of the 1DVAR.
文摘为优化地下水水质监测方案,确定北京顺平岩溶水系统岩溶水水质的主要控制指标,快速掌握地下水水质变化,基于2022年顺平岩溶水系统49件岩溶水样品,综合运用统计分析、Piper三线图和熵权水质指数(entropy weight water quality index,EWQI)分析研究区水化学和水质特征,并耦合逐步多元线性回归分析,探讨能代表研究区岩溶水水质的关键指标。结果显示:①顺平岩溶水系统岩溶水具有微碱性、低盐度的特征,水化学类型主要为HCO_(3)^(-)—Ca^(2+)·Mg^(2+)型(73.47%)。超标指标为N_(an)(NH_(3)或NH_(4)^(+)中的N,即氨氮以氮计)、pH值、Fe、Mn和F^(-),超标率分别为10.20%、4.08%、4.08%、4.08%和2.04%。②研究区EWQI平均值为26.33,水质“极好”,其中极好和良好所占比例分别为91.84%和8.16%。③基于地下水水质数据构建的EWQI_(min)模型筛选的关键指标为N_(an)、Fe、Mn、N_(ntr)(硝氮以氮计,硝酸盐中N)和F^(-),其决定系数(R^(2))和百分比误差(PE)分别为0.986和3.88%。表明,EWQI_(min)模型优选指标可以代表顺平岩溶水系统的水质状况,对优化水质监测网等水资源管理提供了参考价值。
