Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for a...Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for almost 45%of all new cases worldwide^([2]).展开更多
According to some main assumptions in the Rouse Formula,it analyzes the applicability of Rouse distribution in the coastal region.Based on the classical Rouse Formula,the linear form of Rouse Formula and the transport...According to some main assumptions in the Rouse Formula,it analyzes the applicability of Rouse distribution in the coastal region.Based on the classical Rouse Formula,the linear form of Rouse Formula and the transport characteristics of offshore sediment were used to take lnz/h,lnc_(a),c_(a),u,lnu and z/h as the independent variables.The multiple liner regression method was used to analyze the influence of the independent variables on the vertical distribution of sediment concentration.By using the method of significance test,the factors(ln𝑢)that have less influence on sediment concentration among 6 variables were eliminated.The correlation coefficient between the calculated sediment concentration and the measured sediment concentration indicates that the adopted variables can reflect the characteristics of vertical distribution of concentration of fine sediment near shore under complex dynamic conditions.展开更多
The rock matrix bulk modulus or its inverse, the compressive coefficient, is an important input parameter for fluid substitution by the Biot-Gassmann equation in reservoir prediction. However, it is not easy to accura...The rock matrix bulk modulus or its inverse, the compressive coefficient, is an important input parameter for fluid substitution by the Biot-Gassmann equation in reservoir prediction. However, it is not easy to accurately estimate the bulk modulus by using conventional methods. In this paper, we present a new linear regression equation for calculating the parameter. In order to get this equation, we first derive a simplified Gassmann equation by using a reasonable assumption in which the compressive coefficient of the saturated pore fluid is much greater than the rock matrix, and, second, we use the Eshelby- Walsh relation to replace the equivalent modulus of a dry rock in the Gassmann equation. Results from the rock physics analysis of rock sample from a carbonate area show that rock matrix compressive coefficients calculated with water-saturated and dry rock samples using the linear regression method are very close （their error is less than 1%）. This means the new method is accurate and reliable.展开更多
Abstract Using the method of stepwise multivariate linear regression (SMLR), the quantitative structure activity relationships (QSAR) of two isomeric series of taxol and its derivatives have been studied. It was foun...Abstract Using the method of stepwise multivariate linear regression (SMLR), the quantitative structure activity relationships (QSAR) of two isomeric series of taxol and its derivatives have been studied. It was found that the molar refractivity of the C3′substituent of the C13 side chain has significant correlation with its activity. We deduce that structural changes in the C3′substituents may be critical to the anticancer function. It would be useful to the design and synthesis of taxol like compounds with improved activities.展开更多
In this paper, based on the theory of parameter estimation, we give a selection method and, in a sense of a good character of the parameter estimation, we think that it is very reasonable. Moreover, we offer a calcula...In this paper, based on the theory of parameter estimation, we give a selection method and, in a sense of a good character of the parameter estimation, we think that it is very reasonable. Moreover, we offer a calculation method of selection statistic and an applied example.展开更多
Rainfall is an important factor in estimating the event mean concentration （EMC） which is used to quantify the washed-off pollutant concentrations from non-point sources （NPSs）. Pollutant loads could also be calcu...Rainfall is an important factor in estimating the event mean concentration （EMC） which is used to quantify the washed-off pollutant concentrations from non-point sources （NPSs）. Pollutant loads could also be calculated using rainfall, catchment area and runoff coefficient. In this study, runoff quantity and quality data gathered from a 28-month monitoring conducted on the road and parking lot sites in Korea were evaluated using multiple linear regression （MLR） to develop equations for estimating pollutant loads and EMCs as a function of rainfall variables. The results revealed that total event rainfall and average rainfall intensity are possible predictors of pollutant loads. Overall, the models are indicators of the high uncertainties of NPSs; perhaps estimation of EMCs and loads could be accurately obtained by means of water quality sampling or a long term monitoring is needed to gather more data that can be used for the development of estimation models.展开更多
Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit, in recent decades, artificial intelligence s...Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit, in recent decades, artificial intelligence systems were employed for developing predictive models to estimate and predict many agriculture processes. In the present study, the predictive capabilities of multiple linear regressions （MLR） and artificial neural networks （ANNs） are evaluated to estimate fruit firmness in six months, including each of nutrients concentrations （nitrogen （N）, potassium （K）, calcium （Ca） and magnesium （Mg）） alone （P1）, com- bination of nutrients concentrations （P2）, nutrient concentration ratios alone （P3）, and combination of nutrient concentrations and nutrient concentration ratios （P4）. The results showed that MLR model estimated fruit firmness more accuracy than ANN model in three datasets （P1, P2 and P4）. However, the application of P3 （N/Ca ratio） as the input dataset in ANN model improved the prediction of fruit firmness than the MLR model. Correlation coefficient and root mean squared error （RMSE） were 0.850 and 0.539 between the measured and the estimated data by the ANN model, respectively. Generally, the ANN model showed greater potential in determining the relationship between 6-mon-fruit firmness and nutrients concentration.展开更多
This paper presents an analysis to forecast the loads of an isolated area where the history of load is not available or the history may not represent the realistic demand of electricity. The analysis is done through l...This paper presents an analysis to forecast the loads of an isolated area where the history of load is not available or the history may not represent the realistic demand of electricity. The analysis is done through linear regression and based on the identification of factors on which electrical load growth depends. To determine the identification factors, areas are selected whose histories of load growth rate known and the load growth deciding factors are similar to those of the isolated area. The proposed analysis is applied to an isolated area of Bangladesh, called Swandip where a past history of electrical load demand is not available and also there is no possibility of connecting the area with the main land grid system.展开更多
This article studies parametric component and nonparametric component estimators in a semiparametric regression model with linear time series errors; their r-th mean consistency and complete consistency are obtained u...This article studies parametric component and nonparametric component estimators in a semiparametric regression model with linear time series errors; their r-th mean consistency and complete consistency are obtained under suitable conditions. Finally, the author shows that the usual weight functions based on nearest neighbor methods satisfy the designed assumptions imposed.展开更多
The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to elimin...The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.展开更多
Multiple linear regression （MLR） method was applied to quantify the effects of the net heat flux （NHF）, the net freshwater flux （NFF） and the wind stress on the mixed layer depth （MLD） of the South China Sea ...Multiple linear regression （MLR） method was applied to quantify the effects of the net heat flux （NHF）, the net freshwater flux （NFF） and the wind stress on the mixed layer depth （MLD） of the South China Sea （SCS） based on the simple ocean data assimilation （SODA） dataset. The spatio-temporal distributions of the MLD, the buoyancy flux （combining the NHF and the NFF） and the wind stress of the SCS were presented. Then using an oceanic vertical mixing model, the MLD after a certain time under the same initial conditions but various pairs of boundary conditions （the three factors） was simulated. Applying the MLR method to the results, regression equations which modeling the relationship between the simulated MLD and the three factors were calculated. The equations indicate that when the NHF was negative, it was the primary driver of the mixed layer deepening; and when the NHF was positive, the wind stress played a more important role than that of the NHF while the NFF had the least effect. When the NHF was positive, the relative quantitative effects of the wind stress, the NHF, and the NFF were about i0, 6 and 2. The above conclusions were applied to explaining the spatio-temporal distributions of the MLD in the SCS and thus proved to be valid.展开更多
A class of estimators of the mean survival time with interval censored data are studied by unbiased transformation method. The estimators are constructed based on the observations to ensure unbiasedness in the sense t...A class of estimators of the mean survival time with interval censored data are studied by unbiased transformation method. The estimators are constructed based on the observations to ensure unbiasedness in the sense that the estimators in a certain class have the same expectation as the mean survival time. The estimators have good properties such as strong consistency （with the rate of O（n^-1/1 （log log n）^1/2）） and asymptotic normality. The application to linear regression is considered and the simulation reports are given.展开更多
Tegillarca granosa(T.granosa)is susceptible to heavy metals,which may pose a threat to consumer health.Thus,healthy and polluted T.granosa should be distinguished quickly.This study aimed to rapidly identify heavy met...Tegillarca granosa(T.granosa)is susceptible to heavy metals,which may pose a threat to consumer health.Thus,healthy and polluted T.granosa should be distinguished quickly.This study aimed to rapidly identify heavy metal pollution by using laser-induced breakdown spectroscopy(LIBS)coupled with linear regression classification(LRC).Five types of T.granosa were studied,namely,Cd-,Zn-,Pb-contaminated,mixed contaminated,and control samples.Threshold method was applied to extract the significant variables from LIBS spectra.Then,LRC was used to classify the different types of T.granosa.Other classification models and feature selection methods were used for comparison.LRC was the best model,achieving an accuracy of 90.67%.Results indicated that LIBS combined with LRC is effective and feasible for T.granosa heavy metal detection.展开更多
In current paper, a quantitative structure-activity relationship （QSAR） study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of ...In current paper, a quantitative structure-activity relationship （QSAR） study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of 46 compounds and a test set of 10 compounds. The electronic and topological descriptors computed by the Scigress package and Dragon software were used as predictor variables. Multiple linear regression （MLR） and support vector machine （SVM） were utilized to build the linear and nonlinear QSAR models, respectively. The obtained models with five descriptors show strong predictive ability. The linear model fits the training set with R2 = 0.71, with higher SVM values of R2 = 0.77. The validation results obtained from the test set indicate that the SVM model is comparable or superior to that obtained by MLR, both in terms of prediction ability and robustness.展开更多
As a mono-sodium salt form of alendronic acid,alendronate sodium presents multi-level ionization for the dissociation of its four hydroxyl groups.The dissociation constants of alendronate sodium were determined in thi...As a mono-sodium salt form of alendronic acid,alendronate sodium presents multi-level ionization for the dissociation of its four hydroxyl groups.The dissociation constants of alendronate sodium were determined in this work by studying the piecewise linear relationship between volume of titrant and p H value based on acidbase potentiometric titration reaction.The distribution curves of alendronate sodium were drawn according to the determined p Ka values.There were 4 dissociation constants(pKa_1=2.43,pKa_2=7.55,pKa_3=10.80,pKa_4=11.99,respectively) of alendronate sodium,and 12 existing forms,of which 4 could be ignored,existing in different p H environments.展开更多
Support Vector-based learning methods are an important part of Computational Intelligence techniques. Recent efforts have been dealing with the problem of learning from very large datasets. This paper reviews the most...Support Vector-based learning methods are an important part of Computational Intelligence techniques. Recent efforts have been dealing with the problem of learning from very large datasets. This paper reviews the most commonly used formulations of support vector machines for regression (SVRs) aiming to emphasize its usability on large-scale applications. We review the general concept of support vector machines (SVMs), address the state-of-the-art on training methods SVMs, and explain the fundamental principle of SVRs. The most common learning methods for SVRs are introduced and linear programming-based SVR formulations are explained emphasizing its suitability for large-scale learning. Finally, this paper also discusses some open problems and current trends.展开更多
Rivers are important systems which provide water to fulfill human needs. However, excessive human uses over the years have led to deterioration in quality of river causing, causing health problems from contaminated wa...Rivers are important systems which provide water to fulfill human needs. However, excessive human uses over the years have led to deterioration in quality of river causing, causing health problems from contaminated water. This study focuses on the application of statistical techniques, Multiple Linear Regression model and MANOVA to assess health impacts due to pollution in Cauvery river stretch in Srirangapatna. In this study, using Multiple Linear Regression, it is found that health impact level is 60.8% dependent on water quality parameters of BOD, COD, TDS, TC and FC. The t-statistics and their associated 2-tailed p-values indicate that COD and TDS produces health impacts compared to BOD, TC and FC, when their effects are put together across all the six sampling stations in Srirangapatna. Further Pearson correlation Matrix shows highly significant positive correlation amongst parameters across all stations indicating possibility of common sources of origin that might be anthropogenic. Also graphs are plotted for individual parameters across all stations and it reveals that COD and TDS values are significant across all sampling stations, though their values are higher in impact stations, causing health impacts.展开更多
In agricultural systems,the regular monitoring of Soil Organic Matter(SOM)dynamics is essential.This task is costly and time-consuming when using the conventional method,especially in a very fragmented area and with i...In agricultural systems,the regular monitoring of Soil Organic Matter(SOM)dynamics is essential.This task is costly and time-consuming when using the conventional method,especially in a very fragmented area and with intensive agricultural activity,such as the area of Sidi Bennour.The study area is located in the Doukkala irrigated perimeter in Morocco.Satellite data can provide an alternative and fill this gap at a low cost.Models to predict SOM from a satellite image,whether linear or nonlinear,have shown considerable interest.This study aims to compare SOM prediction using Multiple Linear Regression(MLR)and Artificial Neural Networks(ANN).A total of 368 points were collected at a depth of 0-30 cm and analyzed in the laboratory.An image at 15 m resolution(MSPAN)was produced from a 30 m resolution(MS)Landsat-8 image using image pansharpening processing and panchromatic band(15 m).The results obtained show that the MLR models predicted the SOM with(training/validation)R^(2)values of 0.62/0.63 and 0.64/0.65 and RMSE values of 0.23/0.22 and 0.22/0.21 for the MS and MSPAN images,respectively.In contrast,the ANN models predicted SOM with R2 values of 0.65/0.66 and 0.69/0.71 and RMSE values of 0.22/0.10 and 0.21/0.18 for the MS and MSPAN images,respectively.Image pansharpening improved the prediction accuracy by 2.60%and 4.30%and reduced the estimation error by 0.80%and 1.30%for the MLR and ANN models,respectively.展开更多
Foam drilling is increasingly used to develop low pressure reservoirs or highly depleted mature reservoirs because of minimizing the formation damage and potential hazardous drilling problems. Prediction of the cuttin...Foam drilling is increasingly used to develop low pressure reservoirs or highly depleted mature reservoirs because of minimizing the formation damage and potential hazardous drilling problems. Prediction of the cuttings concentration in the wellbore annulus as a function of operational drilling parameters such as wellbore geometry, pumping rate, drilling fluid rheology and density and maximum drilling rate is very important for optimizing these parameters. This paper describes a simple and more reliable artificial neural network (ANN) method and multiple linear regression (MLR) to predict cuttings concentration during foam drilling operation. This model is applicable for various borehole conditions using some critical parameters associated with foam velocity, foam quality, hole geometry, subsurface condition (pressure and temperature) and pipe rotation. The average absolute percent relative error (AAPE) between the experimental cuttings concentration and ANN model is less than 6%, and using MLR, AAPE is less than 9%. A comparison of the ANN and mechanistic model was done. The AAPE values for all datasets in this study were 3.2%, 8.5% and 10.3% for ANN model, MLR model and mechanistic model respectively. The results show high ability of ANN in prediction with respect to statistical methods.展开更多
In this paper we consider the empirical Bayes (EB) estimation problem for estimable function of regression coefficient in a multiple linear regression model Y=Xβ+e. where e with given β has a multivariate standard n...In this paper we consider the empirical Bayes (EB) estimation problem for estimable function of regression coefficient in a multiple linear regression model Y=Xβ+e. where e with given β has a multivariate standard normal distribution. We get the EB estimators by using kernel estimation of multivariate density function and its first order partial derivatives. It is shown that the convergence rates of the EB estimators are under the condition where an integer k > 1 . is an arbitrary small number and m is the dimension of the vector Y.展开更多
基金supported by the Natural Science Foundation of Shanghai(23ZR1463600)Shanghai Pudong New Area Health Commission Research Project(PW2021A-69)Research Project of Clinical Research Center of Shanghai Health Medical University(22MC2022002)。
文摘Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for almost 45%of all new cases worldwide^([2]).
文摘According to some main assumptions in the Rouse Formula,it analyzes the applicability of Rouse distribution in the coastal region.Based on the classical Rouse Formula,the linear form of Rouse Formula and the transport characteristics of offshore sediment were used to take lnz/h,lnc_(a),c_(a),u,lnu and z/h as the independent variables.The multiple liner regression method was used to analyze the influence of the independent variables on the vertical distribution of sediment concentration.By using the method of significance test,the factors(ln𝑢)that have less influence on sediment concentration among 6 variables were eliminated.The correlation coefficient between the calculated sediment concentration and the measured sediment concentration indicates that the adopted variables can reflect the characteristics of vertical distribution of concentration of fine sediment near shore under complex dynamic conditions.
基金supported by the National Nature Science Foundation of China (Grant Noss 40739907 and 40774064)National Science and Technology Major Project (Grant No. 2008ZX05025-003)
文摘The rock matrix bulk modulus or its inverse, the compressive coefficient, is an important input parameter for fluid substitution by the Biot-Gassmann equation in reservoir prediction. However, it is not easy to accurately estimate the bulk modulus by using conventional methods. In this paper, we present a new linear regression equation for calculating the parameter. In order to get this equation, we first derive a simplified Gassmann equation by using a reasonable assumption in which the compressive coefficient of the saturated pore fluid is much greater than the rock matrix, and, second, we use the Eshelby- Walsh relation to replace the equivalent modulus of a dry rock in the Gassmann equation. Results from the rock physics analysis of rock sample from a carbonate area show that rock matrix compressive coefficients calculated with water-saturated and dry rock samples using the linear regression method are very close （their error is less than 1%）. This means the new method is accurate and reliable.
文摘Abstract Using the method of stepwise multivariate linear regression (SMLR), the quantitative structure activity relationships (QSAR) of two isomeric series of taxol and its derivatives have been studied. It was found that the molar refractivity of the C3′substituent of the C13 side chain has significant correlation with its activity. We deduce that structural changes in the C3′substituents may be critical to the anticancer function. It would be useful to the design and synthesis of taxol like compounds with improved activities.
基金Supported by the Natural Science Foundation of Anhui Education Committee
文摘In this paper, based on the theory of parameter estimation, we give a selection method and, in a sense of a good character of the parameter estimation, we think that it is very reasonable. Moreover, we offer a calculation method of selection statistic and an applied example.
基金provided by the Korean Ministry of Environment and Eco Star Project
文摘Rainfall is an important factor in estimating the event mean concentration （EMC） which is used to quantify the washed-off pollutant concentrations from non-point sources （NPSs）. Pollutant loads could also be calculated using rainfall, catchment area and runoff coefficient. In this study, runoff quantity and quality data gathered from a 28-month monitoring conducted on the road and parking lot sites in Korea were evaluated using multiple linear regression （MLR） to develop equations for estimating pollutant loads and EMCs as a function of rainfall variables. The results revealed that total event rainfall and average rainfall intensity are possible predictors of pollutant loads. Overall, the models are indicators of the high uncertainties of NPSs; perhaps estimation of EMCs and loads could be accurately obtained by means of water quality sampling or a long term monitoring is needed to gather more data that can be used for the development of estimation models.
文摘Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit, in recent decades, artificial intelligence systems were employed for developing predictive models to estimate and predict many agriculture processes. In the present study, the predictive capabilities of multiple linear regressions （MLR） and artificial neural networks （ANNs） are evaluated to estimate fruit firmness in six months, including each of nutrients concentrations （nitrogen （N）, potassium （K）, calcium （Ca） and magnesium （Mg）） alone （P1）, com- bination of nutrients concentrations （P2）, nutrient concentration ratios alone （P3）, and combination of nutrient concentrations and nutrient concentration ratios （P4）. The results showed that MLR model estimated fruit firmness more accuracy than ANN model in three datasets （P1, P2 and P4）. However, the application of P3 （N/Ca ratio） as the input dataset in ANN model improved the prediction of fruit firmness than the MLR model. Correlation coefficient and root mean squared error （RMSE） were 0.850 and 0.539 between the measured and the estimated data by the ANN model, respectively. Generally, the ANN model showed greater potential in determining the relationship between 6-mon-fruit firmness and nutrients concentration.
文摘This paper presents an analysis to forecast the loads of an isolated area where the history of load is not available or the history may not represent the realistic demand of electricity. The analysis is done through linear regression and based on the identification of factors on which electrical load growth depends. To determine the identification factors, areas are selected whose histories of load growth rate known and the load growth deciding factors are similar to those of the isolated area. The proposed analysis is applied to an isolated area of Bangladesh, called Swandip where a past history of electrical load demand is not available and also there is no possibility of connecting the area with the main land grid system.
基金This article was supported by the National Natural Science Foundation of China(10571001)the Innovation Group Foundation of Anhui University
文摘This article studies parametric component and nonparametric component estimators in a semiparametric regression model with linear time series errors; their r-th mean consistency and complete consistency are obtained under suitable conditions. Finally, the author shows that the usual weight functions based on nearest neighbor methods satisfy the designed assumptions imposed.
基金supported by the National Natural Science Foundation of China(71071077)the Ministry of Education Key Project of National Educational Science Planning(DFA090215)+1 种基金China Postdoctoral Science Foundation(20100481137)Funding of Jiangsu Innovation Program for Graduate Education(CXZZ11-0226)
文摘The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.
基金The National Natural Science Foundation of China under contract No.11174235the Science and Technology Development Project of Shaanxi Province of China under contract No.2010KJXX-02+2 种基金the Program for New Century Excellent Talents in University of China under contract No. NCET-08-0455the Science and Technology Innovation Foundation of Northwestern Polytechnical University of Chinathe Doctorate Foundation of Northwestern Polytechnical University of China under contract No.CX201226.
文摘Multiple linear regression （MLR） method was applied to quantify the effects of the net heat flux （NHF）, the net freshwater flux （NFF） and the wind stress on the mixed layer depth （MLD） of the South China Sea （SCS） based on the simple ocean data assimilation （SODA） dataset. The spatio-temporal distributions of the MLD, the buoyancy flux （combining the NHF and the NFF） and the wind stress of the SCS were presented. Then using an oceanic vertical mixing model, the MLD after a certain time under the same initial conditions but various pairs of boundary conditions （the three factors） was simulated. Applying the MLR method to the results, regression equations which modeling the relationship between the simulated MLD and the three factors were calculated. The equations indicate that when the NHF was negative, it was the primary driver of the mixed layer deepening; and when the NHF was positive, the wind stress played a more important role than that of the NHF while the NFF had the least effect. When the NHF was positive, the relative quantitative effects of the wind stress, the NHF, and the NFF were about i0, 6 and 2. The above conclusions were applied to explaining the spatio-temporal distributions of the MLD in the SCS and thus proved to be valid.
基金Supported by the National Natural Science Foundation of China (70171008)
文摘A class of estimators of the mean survival time with interval censored data are studied by unbiased transformation method. The estimators are constructed based on the observations to ensure unbiasedness in the sense that the estimators in a certain class have the same expectation as the mean survival time. The estimators have good properties such as strong consistency （with the rate of O（n^-1/1 （log log n）^1/2）） and asymptotic normality. The application to linear regression is considered and the simulation reports are given.
基金This research was funded by National Natural Science Foundation of China(Nos.31571920,61671378)。
文摘Tegillarca granosa(T.granosa)is susceptible to heavy metals,which may pose a threat to consumer health.Thus,healthy and polluted T.granosa should be distinguished quickly.This study aimed to rapidly identify heavy metal pollution by using laser-induced breakdown spectroscopy(LIBS)coupled with linear regression classification(LRC).Five types of T.granosa were studied,namely,Cd-,Zn-,Pb-contaminated,mixed contaminated,and control samples.Threshold method was applied to extract the significant variables from LIBS spectra.Then,LRC was used to classify the different types of T.granosa.Other classification models and feature selection methods were used for comparison.LRC was the best model,achieving an accuracy of 90.67%.Results indicated that LIBS combined with LRC is effective and feasible for T.granosa heavy metal detection.
基金Supported by the Ministry of Environmental Protection of China(No.2011467037)
文摘In current paper, a quantitative structure-activity relationship （QSAR） study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of 46 compounds and a test set of 10 compounds. The electronic and topological descriptors computed by the Scigress package and Dragon software were used as predictor variables. Multiple linear regression （MLR） and support vector machine （SVM） were utilized to build the linear and nonlinear QSAR models, respectively. The obtained models with five descriptors show strong predictive ability. The linear model fits the training set with R2 = 0.71, with higher SVM values of R2 = 0.77. The validation results obtained from the test set indicate that the SVM model is comparable or superior to that obtained by MLR, both in terms of prediction ability and robustness.
基金the support of Key Laboratory of Chinese Medicine Preparation of Solid Dispersion,Gansu Longshenrongfa Pharmaceutical Industry Co.,Ltd.,Gansu Province,China
文摘As a mono-sodium salt form of alendronic acid,alendronate sodium presents multi-level ionization for the dissociation of its four hydroxyl groups.The dissociation constants of alendronate sodium were determined in this work by studying the piecewise linear relationship between volume of titrant and p H value based on acidbase potentiometric titration reaction.The distribution curves of alendronate sodium were drawn according to the determined p Ka values.There were 4 dissociation constants(pKa_1=2.43,pKa_2=7.55,pKa_3=10.80,pKa_4=11.99,respectively) of alendronate sodium,and 12 existing forms,of which 4 could be ignored,existing in different p H environments.
文摘Support Vector-based learning methods are an important part of Computational Intelligence techniques. Recent efforts have been dealing with the problem of learning from very large datasets. This paper reviews the most commonly used formulations of support vector machines for regression (SVRs) aiming to emphasize its usability on large-scale applications. We review the general concept of support vector machines (SVMs), address the state-of-the-art on training methods SVMs, and explain the fundamental principle of SVRs. The most common learning methods for SVRs are introduced and linear programming-based SVR formulations are explained emphasizing its suitability for large-scale learning. Finally, this paper also discusses some open problems and current trends.
文摘Rivers are important systems which provide water to fulfill human needs. However, excessive human uses over the years have led to deterioration in quality of river causing, causing health problems from contaminated water. This study focuses on the application of statistical techniques, Multiple Linear Regression model and MANOVA to assess health impacts due to pollution in Cauvery river stretch in Srirangapatna. In this study, using Multiple Linear Regression, it is found that health impact level is 60.8% dependent on water quality parameters of BOD, COD, TDS, TC and FC. The t-statistics and their associated 2-tailed p-values indicate that COD and TDS produces health impacts compared to BOD, TC and FC, when their effects are put together across all the six sampling stations in Srirangapatna. Further Pearson correlation Matrix shows highly significant positive correlation amongst parameters across all stations indicating possibility of common sources of origin that might be anthropogenic. Also graphs are plotted for individual parameters across all stations and it reveals that COD and TDS values are significant across all sampling stations, though their values are higher in impact stations, causing health impacts.
文摘In agricultural systems,the regular monitoring of Soil Organic Matter(SOM)dynamics is essential.This task is costly and time-consuming when using the conventional method,especially in a very fragmented area and with intensive agricultural activity,such as the area of Sidi Bennour.The study area is located in the Doukkala irrigated perimeter in Morocco.Satellite data can provide an alternative and fill this gap at a low cost.Models to predict SOM from a satellite image,whether linear or nonlinear,have shown considerable interest.This study aims to compare SOM prediction using Multiple Linear Regression(MLR)and Artificial Neural Networks(ANN).A total of 368 points were collected at a depth of 0-30 cm and analyzed in the laboratory.An image at 15 m resolution(MSPAN)was produced from a 30 m resolution(MS)Landsat-8 image using image pansharpening processing and panchromatic band(15 m).The results obtained show that the MLR models predicted the SOM with(training/validation)R^(2)values of 0.62/0.63 and 0.64/0.65 and RMSE values of 0.23/0.22 and 0.22/0.21 for the MS and MSPAN images,respectively.In contrast,the ANN models predicted SOM with R2 values of 0.65/0.66 and 0.69/0.71 and RMSE values of 0.22/0.10 and 0.21/0.18 for the MS and MSPAN images,respectively.Image pansharpening improved the prediction accuracy by 2.60%and 4.30%and reduced the estimation error by 0.80%and 1.30%for the MLR and ANN models,respectively.
文摘Foam drilling is increasingly used to develop low pressure reservoirs or highly depleted mature reservoirs because of minimizing the formation damage and potential hazardous drilling problems. Prediction of the cuttings concentration in the wellbore annulus as a function of operational drilling parameters such as wellbore geometry, pumping rate, drilling fluid rheology and density and maximum drilling rate is very important for optimizing these parameters. This paper describes a simple and more reliable artificial neural network (ANN) method and multiple linear regression (MLR) to predict cuttings concentration during foam drilling operation. This model is applicable for various borehole conditions using some critical parameters associated with foam velocity, foam quality, hole geometry, subsurface condition (pressure and temperature) and pipe rotation. The average absolute percent relative error (AAPE) between the experimental cuttings concentration and ANN model is less than 6%, and using MLR, AAPE is less than 9%. A comparison of the ANN and mechanistic model was done. The AAPE values for all datasets in this study were 3.2%, 8.5% and 10.3% for ANN model, MLR model and mechanistic model respectively. The results show high ability of ANN in prediction with respect to statistical methods.
文摘In this paper we consider the empirical Bayes (EB) estimation problem for estimable function of regression coefficient in a multiple linear regression model Y=Xβ+e. where e with given β has a multivariate standard normal distribution. We get the EB estimators by using kernel estimation of multivariate density function and its first order partial derivatives. It is shown that the convergence rates of the EB estimators are under the condition where an integer k > 1 . is an arbitrary small number and m is the dimension of the vector Y.
