In this study,the 24 h tensile strength of new type acetone-urea-formaldehyde furan resin (nitrogen content 3%) was investigated by uniform design optimization.Four independent variables such as acetone:formaldehyde m...In this study,the 24 h tensile strength of new type acetone-urea-formaldehyde furan resin (nitrogen content 3%) was investigated by uniform design optimization.Four independent variables such as acetone:formaldehyde molar ratio (mol/mol),solution pH value,reaction temperature (℃) and reaction time (min) were considered in the experiments.U13(134) uniform design was employed and the equation of 24 h tensile strength model was obtained after 13 experimentations.The 24 h tensile strength was optimized by applying single factor experiments and stepwise non-linear regression analysis.Minitab (Minitab 15 trial version) and MATLAB (R2010a trial version) were used for data analysis.The t-value and p-value indicate that the major impact factors include the interaction effect of solution pH value and reaction temperature (X2X3),the linear terms of acetone:formaldehyde molar ratio (X1),reaction time (X4) followed by the square effects of acetone/formaldehyde molar ratio (X1X1).The optimized results were achieved with the acetone:formaldehyde molar ratio (mol/mol) at 3:1,solution pH value at 6.0,reaction temperature at 70℃,and reaction time at 140 min,respectively.This method can not only significantly reduce the number and cost of the tests,but also provide a good experimental design strategy for the development of furan resin.The investigation shows that the predicted results of 24 h tensile strength are consistent well with the experimental ones.展开更多
In this paper, a regression method of estimation has been used to derive the mean estimate of the survey variable using simple random sampling without replacement in the presence of observational errors. Two covariate...In this paper, a regression method of estimation has been used to derive the mean estimate of the survey variable using simple random sampling without replacement in the presence of observational errors. Two covariates were used and a case where the observational errors were in both the survey variable and the covariates was considered. The inclusion of observational errors was due to the fact that data collected through surveys are often not free from errors that occur during observation. These errors can occur due to over-reporting, under-reporting, memory failure by the respondents or use of imprecise tools of data collection. The expression of mean squared error (MSE) based on the obtained estimator has been derived to the first degree of approximation. The results of a simulation study show that the derived modified regression mean estimator under observational errors is more efficient than the mean per unit estimator and some other existing estimators. The proposed estimator can therefore be used in estimating a finite population mean, while considering observational errors that may occur during a study.展开更多
The single-index model with monotonic link function is investigated. Firstly, it is showed that the link function h（.） can be viewed by a graphic method. That is, the plot with the fitted response y on the horizonta...The single-index model with monotonic link function is investigated. Firstly, it is showed that the link function h（.） can be viewed by a graphic method. That is, the plot with the fitted response y on the horizontal axis and the observed y on the vertical axis can be used to visualize the link function. It is pointed out that this graphic approach is also applicable even when the link function is not monotonic. Note that many existing nonparametric smoothers can also be used to assess h（.）. Therefore, the I-spline approximation of the link function via maximizing the covariance function with a penalty function is investigated in the present work. The consistency of the criterion is constructed. A small simulation is carried out to evidence the efficiency of the approach proposed in the paper.展开更多
COVID-19 is a pandemic that has affected nearly every country in the world.At present,sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans.H...COVID-19 is a pandemic that has affected nearly every country in the world.At present,sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans.However,widespread diseases,such as COVID-19,create numerous challenges to this goal,and some of those challenges are not yet defined.In this study,a Shallow Single-Layer Perceptron Neural Network(SSLPNN)and Gaussian Process Regression(GPR)model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions:namely,China,South Korea,Japan,Saudi Arabia,and Pakistan.Significant environmental and non-environmental features were taken as the input dataset,and confirmed COVID-19 cases were taken as the output dataset.A correlation analysis was done to identify patterns in the cases related to fluctuations in the associated variables.The results of this study established that the population and air quality index of a region had a statistically significant influence on the cases.However,age and the human development index had a negative influence on the cases.The proposed SSLPNN-based classification model performed well when predicting the classes of confirmed cases.During training,the binary classification model was highly accurate,with a Root Mean Square Error(RMSE)of 0.91.Likewise,the results of the regression analysis using the GPR technique with Matern 5/2 were highly accurate(RMSE=0.95239)when predicting the number of confirmed COVID-19 cases in an area.However,dynamic management has occupied a core place in studies on the sustainable development of public health but dynamic management depends on proactive strategies based on statistically verified approaches,like Artificial Intelligence(AI).In this study,an SSLPNN model has been trained to fit public health associated data into an appropriate class,allowing GPR to predict the number of confirmed COVID-19 cases in an area based on the given展开更多
This paper presents a modeling procedure for deriving a single value measurebased on a regression model, and a method for determining a statistical threshold value asidentification criterion of normal or abnormal stat...This paper presents a modeling procedure for deriving a single value measurebased on a regression model, and a method for determining a statistical threshold value asidentification criterion of normal or abnormal states of machine wear. A real numerical example isexamined by the method and identification criterion presented. The results indicate that thejudgments by the presented methods are basically consistent with the real facts, and therefore themethod and identification criterion are valuable for judging the normal or abnormal state of machinewear based on oil analysis.展开更多
Understanding the temporal stability in the factors influencing drivers' injury severity in single-vehicle collisions would help evaluating the effectiveness of implementing different safety treatments so that res...Understanding the temporal stability in the factors influencing drivers' injury severity in single-vehicle collisions would help evaluating the effectiveness of implementing different safety treatments so that researchers could understand whether any safety improvements,observed after applying a certain safety treatment, are attributed to the specific treatment or simply attributed to the temporal instability of the factors being addressed. This study investigates the temporal stability of the factors affecting drivers' injury severity in singlevehicle collisions involving light-duty vehicles. The study is based on utilizing ordinal regression modeling to analyze the severity of drivers' injuries in all police-reported lightduty single-vehicle collisions that occurred in North Carolina from January 1, 2007, to December 31, 2013. A separate regression model was estimated for each year so that statistical significance of each risk factor may be compared over the years. The study also estimated random-parameter(mixed) ordered logit models to explore the heterogeneity in data. The most significant factor that was found to increase the severity of drivers' injuries in light-duty single-vehicle collisions is driving under the influence of alcohol or illicit drugs. Other significant factors, in decreasing order in terms of their significance, include driving on a highway curve, exceeding speed limit, lighting conditions, the age of the driver, and the age of the vehicle. In contrast, there were six factors that were found to be significant in only some years and not in all years. These six temporally unstable factors include the use of seatbelt, driver's gender, rural highways, undivided highways, the type of the light-duty vehicle, and weather and road surface conditions. These same factors were found by other previous research studies to be significant and stable predictors of drivers' injury severity in single-vehicle collisions.展开更多
启发于过完备字典中稀疏线性组合的高分辨率图像的块与其对应的低分辨率局部块能很好地匹配,提出一种回归函数结合局部自相似的单帧图像超分辨率算法;该算法结合了实例图像块的学习和局部自相似图像块的学习,实例图像块的局部回归避免...启发于过完备字典中稀疏线性组合的高分辨率图像的块与其对应的低分辨率局部块能很好地匹配,提出一种回归函数结合局部自相似的单帧图像超分辨率算法;该算法结合了实例图像块的学习和局部自相似图像块的学习,实例图像块的局部回归避免了从低分辨率到高分辨率图像块映射的病态性问题;通过局部自相似实例图像块学习获得非线性映射函数的一阶近似,从而获得低分辨率图像块相对应的高分辨率图像块,克服了实例图像块算法不足的问题;实验采用峰值信噪比(Peak Signal to Noise Ratio,PSNR)和均方误差(Root-mean-square error,RMSE)比较各算法效果;从实验结果数据可以看出,大多数情况下,提出的算法具有最高的峰值信噪比和最低的均方根误差,从实验结果图可以看出,提出的算法的纹理保留的最好,图像自然性最好,且运行时间也少于其他几种较新的算法,表明提出的算法更适合用于解决实际问题。展开更多
In this research,the thermal performance of a single U-tube vertical ground heat exchanger is evaluated numerically as a function of the most influential flow parameters,namely,the soil porosity,volumetric heat capaci...In this research,the thermal performance of a single U-tube vertical ground heat exchanger is evaluated numerically as a function of the most influential flow parameters,namely,the soil porosity,volumetric heat capacity,and thermal conductivity of the backfill material,inlet volume flow rate,and inlet fluid temperature.The results are discussed in terms of the variations of the heat exchange rate,the effective thermal resistance,and the effectiveness of the ground heat exchanger.They show that the inlet volume flow rate,inlet fluid temperature,and backfill material thermal conductivity have significant effects on the thermal performance of the ground heat exchanger,such that by decreasing the inlet volume flow rate and increasing the backfill material thermal conductivity and inlet fluid temperature,the outlet fluid temperature decreases considerably.On the contrary,the soil porosity and backfill material volumetric heat capacity have negligible effects on the studied ground heat exchanger’s thermal performance.The lowest inlet fluid temperature reaches a the maximum effective thermal resistance of borehole and soil,and consequently the minimum heat transfer rate and effectiveness.Also,multilinear regression analyses are performed to determine the most feasible models able to predict the thermal properties of the single U-tube ground heat exchanger.展开更多
文摘In this study,the 24 h tensile strength of new type acetone-urea-formaldehyde furan resin (nitrogen content 3%) was investigated by uniform design optimization.Four independent variables such as acetone:formaldehyde molar ratio (mol/mol),solution pH value,reaction temperature (℃) and reaction time (min) were considered in the experiments.U13(134) uniform design was employed and the equation of 24 h tensile strength model was obtained after 13 experimentations.The 24 h tensile strength was optimized by applying single factor experiments and stepwise non-linear regression analysis.Minitab (Minitab 15 trial version) and MATLAB (R2010a trial version) were used for data analysis.The t-value and p-value indicate that the major impact factors include the interaction effect of solution pH value and reaction temperature (X2X3),the linear terms of acetone:formaldehyde molar ratio (X1),reaction time (X4) followed by the square effects of acetone/formaldehyde molar ratio (X1X1).The optimized results were achieved with the acetone:formaldehyde molar ratio (mol/mol) at 3:1,solution pH value at 6.0,reaction temperature at 70℃,and reaction time at 140 min,respectively.This method can not only significantly reduce the number and cost of the tests,but also provide a good experimental design strategy for the development of furan resin.The investigation shows that the predicted results of 24 h tensile strength are consistent well with the experimental ones.
文摘In this paper, a regression method of estimation has been used to derive the mean estimate of the survey variable using simple random sampling without replacement in the presence of observational errors. Two covariates were used and a case where the observational errors were in both the survey variable and the covariates was considered. The inclusion of observational errors was due to the fact that data collected through surveys are often not free from errors that occur during observation. These errors can occur due to over-reporting, under-reporting, memory failure by the respondents or use of imprecise tools of data collection. The expression of mean squared error (MSE) based on the obtained estimator has been derived to the first degree of approximation. The results of a simulation study show that the derived modified regression mean estimator under observational errors is more efficient than the mean per unit estimator and some other existing estimators. The proposed estimator can therefore be used in estimating a finite population mean, while considering observational errors that may occur during a study.
基金Supported by the National Natural science Foundation of China(10701035)ChenGuang Project of Shang-hai Education Development Foundation(2007CG33)a Special Fund for Young Teachers in Shanghai Universities(79001320)
文摘The single-index model with monotonic link function is investigated. Firstly, it is showed that the link function h（.） can be viewed by a graphic method. That is, the plot with the fitted response y on the horizontal axis and the observed y on the vertical axis can be used to visualize the link function. It is pointed out that this graphic approach is also applicable even when the link function is not monotonic. Note that many existing nonparametric smoothers can also be used to assess h（.）. Therefore, the I-spline approximation of the link function via maximizing the covariance function with a penalty function is investigated in the present work. The consistency of the criterion is constructed. A small simulation is carried out to evidence the efficiency of the approach proposed in the paper.
文摘COVID-19 is a pandemic that has affected nearly every country in the world.At present,sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans.However,widespread diseases,such as COVID-19,create numerous challenges to this goal,and some of those challenges are not yet defined.In this study,a Shallow Single-Layer Perceptron Neural Network(SSLPNN)and Gaussian Process Regression(GPR)model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions:namely,China,South Korea,Japan,Saudi Arabia,and Pakistan.Significant environmental and non-environmental features were taken as the input dataset,and confirmed COVID-19 cases were taken as the output dataset.A correlation analysis was done to identify patterns in the cases related to fluctuations in the associated variables.The results of this study established that the population and air quality index of a region had a statistically significant influence on the cases.However,age and the human development index had a negative influence on the cases.The proposed SSLPNN-based classification model performed well when predicting the classes of confirmed cases.During training,the binary classification model was highly accurate,with a Root Mean Square Error(RMSE)of 0.91.Likewise,the results of the regression analysis using the GPR technique with Matern 5/2 were highly accurate(RMSE=0.95239)when predicting the number of confirmed COVID-19 cases in an area.However,dynamic management has occupied a core place in studies on the sustainable development of public health but dynamic management depends on proactive strategies based on statistically verified approaches,like Artificial Intelligence(AI).In this study,an SSLPNN model has been trained to fit public health associated data into an appropriate class,allowing GPR to predict the number of confirmed COVID-19 cases in an area based on the given
文摘This paper presents a modeling procedure for deriving a single value measurebased on a regression model, and a method for determining a statistical threshold value asidentification criterion of normal or abnormal states of machine wear. A real numerical example isexamined by the method and identification criterion presented. The results indicate that thejudgments by the presented methods are basically consistent with the real facts, and therefore themethod and identification criterion are valuable for judging the normal or abnormal state of machinewear based on oil analysis.
基金financially supported by a Science and Engineering Research Grant provided by the Emirates Foundation
文摘Understanding the temporal stability in the factors influencing drivers' injury severity in single-vehicle collisions would help evaluating the effectiveness of implementing different safety treatments so that researchers could understand whether any safety improvements,observed after applying a certain safety treatment, are attributed to the specific treatment or simply attributed to the temporal instability of the factors being addressed. This study investigates the temporal stability of the factors affecting drivers' injury severity in singlevehicle collisions involving light-duty vehicles. The study is based on utilizing ordinal regression modeling to analyze the severity of drivers' injuries in all police-reported lightduty single-vehicle collisions that occurred in North Carolina from January 1, 2007, to December 31, 2013. A separate regression model was estimated for each year so that statistical significance of each risk factor may be compared over the years. The study also estimated random-parameter(mixed) ordered logit models to explore the heterogeneity in data. The most significant factor that was found to increase the severity of drivers' injuries in light-duty single-vehicle collisions is driving under the influence of alcohol or illicit drugs. Other significant factors, in decreasing order in terms of their significance, include driving on a highway curve, exceeding speed limit, lighting conditions, the age of the driver, and the age of the vehicle. In contrast, there were six factors that were found to be significant in only some years and not in all years. These six temporally unstable factors include the use of seatbelt, driver's gender, rural highways, undivided highways, the type of the light-duty vehicle, and weather and road surface conditions. These same factors were found by other previous research studies to be significant and stable predictors of drivers' injury severity in single-vehicle collisions.
文摘启发于过完备字典中稀疏线性组合的高分辨率图像的块与其对应的低分辨率局部块能很好地匹配,提出一种回归函数结合局部自相似的单帧图像超分辨率算法;该算法结合了实例图像块的学习和局部自相似图像块的学习,实例图像块的局部回归避免了从低分辨率到高分辨率图像块映射的病态性问题;通过局部自相似实例图像块学习获得非线性映射函数的一阶近似,从而获得低分辨率图像块相对应的高分辨率图像块,克服了实例图像块算法不足的问题;实验采用峰值信噪比(Peak Signal to Noise Ratio,PSNR)和均方误差(Root-mean-square error,RMSE)比较各算法效果;从实验结果数据可以看出,大多数情况下,提出的算法具有最高的峰值信噪比和最低的均方根误差,从实验结果图可以看出,提出的算法的纹理保留的最好,图像自然性最好,且运行时间也少于其他几种较新的算法,表明提出的算法更适合用于解决实际问题。
文摘In this research,the thermal performance of a single U-tube vertical ground heat exchanger is evaluated numerically as a function of the most influential flow parameters,namely,the soil porosity,volumetric heat capacity,and thermal conductivity of the backfill material,inlet volume flow rate,and inlet fluid temperature.The results are discussed in terms of the variations of the heat exchange rate,the effective thermal resistance,and the effectiveness of the ground heat exchanger.They show that the inlet volume flow rate,inlet fluid temperature,and backfill material thermal conductivity have significant effects on the thermal performance of the ground heat exchanger,such that by decreasing the inlet volume flow rate and increasing the backfill material thermal conductivity and inlet fluid temperature,the outlet fluid temperature decreases considerably.On the contrary,the soil porosity and backfill material volumetric heat capacity have negligible effects on the studied ground heat exchanger’s thermal performance.The lowest inlet fluid temperature reaches a the maximum effective thermal resistance of borehole and soil,and consequently the minimum heat transfer rate and effectiveness.Also,multilinear regression analyses are performed to determine the most feasible models able to predict the thermal properties of the single U-tube ground heat exchanger.
