Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi...Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi166） and wild type （Zhonghua 11） rice. Furthermore, rice lines transformed with protein gene （OsTCTP） and regulation gene （Osmi166） were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.展开更多
Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperatur...Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperature, and precipitation will affect grain protein contents and these factors usually cannot be monitored accurately by remote sensing data from single image. In this research, the relationships between wheat protein content at maturity and wheat agronomic parameters at different growing stages were analyzed and multi-temporal images of Landsat TM were used to estimate grain protein content by partial least squares regression. Experiment data were acquired in the suburb of Beijing during a 2-yr experiment in the period from 2003 to 2004. Determination coefficient, average deviation of self-modeling, and deviation of cross- validation were employed to assess the estimation accuracy of wheat grain protein content. Their values were 0.88, 1.30%, 3.81% and 0.72, 5.22%, 12.36% for 2003 and 2004, respectively. The research laid an agronomic foundation for GPC （grain protein content） estimation by multi-temporal remote sensing. The results showed that it is feasible to estimate GPC of wheat from multi-temporal remote sensing data in large area.展开更多
Detecting plant health conditions plays a key role in farm pest management and crop protection.In this study,measurement of hyperspectral leaf reflectance in rice crop(Oryzasativa L.)was conducted on groups of healthy...Detecting plant health conditions plays a key role in farm pest management and crop protection.In this study,measurement of hyperspectral leaf reflectance in rice crop(Oryzasativa L.)was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae(Helminthosporium oryzae Breda.de Hann)through the wavelength range from 350 to 2500 nm.The percentage of leaf surface lesions was estimated and defined as the disease severity.Statistical methods like multiple stepwise regression,principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level.Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps.The root mean square errors(RMSEs)for training(n=210)and testing(n=53)dataset were 6.5%and 5.8%,respectively.Principal component analysis showed that the first principal component could explain approximately 80%of the variance of the original hyperspectral reflectance.The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3%and 13.9%for the training and testing dataset,respec-tively.Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1%and 2.0%for the training and testing dataset,respectively.Our research demon-strates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.展开更多
To predict the economic loss of crops caused by acid rain,we used partial least squares(PLS) regression to build a model of single dependent variable -the economic loss calculated with the decrease in yield related to...To predict the economic loss of crops caused by acid rain,we used partial least squares(PLS) regression to build a model of single dependent variable -the economic loss calculated with the decrease in yield related to the pH value and levels of Ca2+,NH4+,Na+,K+,Mg2+,SO42-,NO3-,and Cl-in acid rain. We selected vegetables which were sensitive to acid rain as the sample crops,and collected 12 groups of data,of which 8 groups were used for modeling and 4 groups for testing. Using the cross validation method to evaluate the performace of this prediction model indicates that the optimum number of principal components was 3,determined by the minimum of prediction residual error sum of squares,and the prediction error of the regression equation ranges from -2.25% to 4.32%. The model predicted that the economic loss of vegetables from acid rain is negatively corrrelated to pH and the concentrations of NH4+,SO42-,NO3-,and Cl-in the rain,and positively correlated to the concentrations of Ca2+,Na+,K+ and Mg2+. The precision of the model may be improved if the non-linearity of original data is addressed.展开更多
In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically ind...In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares （PLS） regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and influencing factors demonstrate the land use character of rural industrialization and urbanization in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.展开更多
With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistica...With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.展开更多
The water distribution system of one residential district in Tianjin is taken as an example to analyze the changes of water quality.Partial least squares(PLS) regression model,in which the turbidity and Fe are regarde...The water distribution system of one residential district in Tianjin is taken as an example to analyze the changes of water quality.Partial least squares(PLS) regression model,in which the turbidity and Fe are regarded as control objectives,is used to establish the statistical model.The experimental results indicate that the PLS regression model has good predicted results of water quality compared with the monitored data.The percentages of absolute relative error(below 15%,20%,30%) are 44.4%,66.7%,100%(turbidity) and 33.3%,44.4%,77.8%(Fe) on the 4th sampling point;77.8%,88.9%,88.9%(turbidity) and 44.4%,55.6%,66.7%(Fe) on the 5th sampling point.展开更多
The computer auxiliary partial least squares is introduced to simultaneously determine the contents of Deoxyschizandin, Schisandrin, r-Schisandrin in the extracted solution of wuweizi. Regression analysis of the exper...The computer auxiliary partial least squares is introduced to simultaneously determine the contents of Deoxyschizandin, Schisandrin, r-Schisandrin in the extracted solution of wuweizi. Regression analysis of the experimental results shows that the average recovery of each component is all in the range from 98.9% to 110.3% , which means the partial least squares regression spectrophotometry can circumvent the overlappirtg of absorption spectrums of mlulti-components, so that sctisfactory results can be obtained without any scrapple pre-separation.展开更多
This paper analyzes the drop transfer process in gas metal arc welding in short-circuit transfer mode （GMAW-S） in order to develop an optimized spatter rate model that can be used on line. According to thermodynamic...This paper analyzes the drop transfer process in gas metal arc welding in short-circuit transfer mode （GMAW-S） in order to develop an optimized spatter rate model that can be used on line. According to thermodynamic characters and practical behavior, a complete arcing process is divided into three sub-processes： arc re-ignition, energy output and shorting preparation. Shorting process is then divided as drop spread, bridge sustention and bridge destabilization. Nine process variables and their distribution are analyzed based on welding experiments with high-speed photos and synchronous current and voltage signals. Method of variation coefficient is used to reflect process consistency and to design characteristic parameters. Partial least square regression （PLSR） is utilized to set up spatter rate model because of severe correlativity among the above characteristic parameters. PLSR is a new multivariate statistical analysis method, in which regression modeling, data simplification and relativity analysis are included in a single algorithm. Experiment results show that the regression equation based on PLSR is effective for on-line predicting spatter rate of its corresponding welding condition.展开更多
Partial least squares(PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this pap...Partial least squares(PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this paper, we present a quantum partial least squares(QPLS) regression algorithm. To solve the high time complexity of the PLS regression, we design a quantum eigenvector search method to speed up principal components and regression parameters construction. Meanwhile, we give a density matrix product method to avoid multiple access to quantum random access memory(QRAM)during building residual matrices. The time and space complexities of the QPLS regression are logarithmic in the independent variable dimension n, the dependent variable dimension w, and the number of variables m. This algorithm achieves exponential speed-ups over the PLS regression on n, m, and w. In addition, the QPLS regression inspires us to explore more potential quantum machine learning applications in future works.展开更多
During the course of calculating the rice evapotranspiration using weather factors,we often find that some independent variables have multiple correlation.The phenomena can lead to the traditional multivariate regress...During the course of calculating the rice evapotranspiration using weather factors,we often find that some independent variables have multiple correlation.The phenomena can lead to the traditional multivariate regression model which based on least square method distortion.And the stability of the model will be lost.The model will be built based on partial least square regression in the paper,through applying the idea of main component analyze and typical correlation analyze,the writer picks up some component from original material.Thus,the writer builds up the model of rice evapotranspiration to solve the multiple correlation among the independent variables (some weather factors).At last,the writer analyses the model in some parts,and gains the satisfied result.展开更多
Pinus densiflora is a pine species native to the Korean peninsula,and seed orchards have supplied mate-rial needed for afforestation in South Korea.Climate vari-ables affecting seed production have not been identified...Pinus densiflora is a pine species native to the Korean peninsula,and seed orchards have supplied mate-rial needed for afforestation in South Korea.Climate vari-ables affecting seed production have not been identified.The purpose of this study was to determine climate variables that influence annual seed production of two seed orchards using multiple linear regression(MLR),elastic net regres-sion(ENR)and partial least square regression(PLSR)mod-els.The PLSR model included 12 climatic variables from 2003 to 2020 and explained 74.3%of the total variation in seed production.It showed better predictive performance(R2=0.662)than the EN(0.516)and the MLR(0.366)mod-els.Among the 12 climatic variables,July temperature two years prior to seed production and July precipitation after one year had the strongest influence on seed production.The time periods indicated by the two variables corresponded to pollen cone initiation and female gametophyte development.The results will be helpful for developing seed collection plans,selecting new orchard sites with favorable climatic conditions,and investigating the relationships between seed production and climatic factors in related pine species.展开更多
In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health infor...In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.展开更多
Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used parti...Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used partial least square regression （PLSR） models to relate forest parameters, i.e. canopy closure density and above ground tree biomass, to Landsat ETM＋ data. The established models were optimized according to the variable importance for projection （VIP） criterion and the bootstrap method, and their performance was compared using several statistical indices. All variables selected by the VIP criterion passed the bootstrap test （p〈0.05）. The simplified models without insignificant variables （VIP 〈1） performed as well as the full model but with less computation time. The relative root mean square error （RMSE%） was 29% for canopy closure density, and 58% for above ground tree biomass. We conclude that PLSR can be an effective method for estimating canopy closure density and above ground biomass.展开更多
This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） prop...This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） proposed a profile least squares estimator for the parametric component and established its asymptotic normality. We further show that the profile least squares estimator can achieve the law of iterated logarithm. Moreover, we study the estimators of the functions characterizing the non-linear part as well as the error variance. The strong convergence rate and the law of iterated logarithm are derived for them, respectively.展开更多
Near infrared （NIR） hyperspectral imaging measurement of sugar content in peach was introauced. NIR spectral images （650～1 000 nm, resolution： 2 nm） of peach samples were captured with developed hyperspectral im...Near infrared （NIR） hyperspectral imaging measurement of sugar content in peach was introauced. NIR spectral images （650～1 000 nm, resolution： 2 nm） of peach samples were captured with developed hyperspectral imaging setup. Partial least square （PLS） regression prediction model was developed to estimate the sugar content in peach; step-wise backward method was utilized to determine optimal wavelength subsets. Experimental results show that the calibration model with optimal wavelength subsets has a correlation coefficient of prediction of 0.97 and a standard error of prediction of 0.19, the prediction accuracy is higher than the calibration model applied over the whole wavelength, which proves that variable selection plays an important role in improving the prediction accuracy of PLS regression model.展开更多
In recent years,nonvascular epiphytic communities have been increasingly subjected to extreme climatic conditions,with heavy rains and prolonged droughts.Therefore,understanding their management of water resources pro...In recent years,nonvascular epiphytic communities have been increasingly subjected to extreme climatic conditions,with heavy rains and prolonged droughts.Therefore,understanding their management of water resources provides insight into their ecosystem-level contributions.However,until now,little has been done to assess this feature at a micro-scale level considering species-species interactions.In this context,this study develops an analytical strategy based on hyperspectral imaging(HSI)and chemometrics to map the water content(WC)of nonvascular epiphytic communities during a dehydration process,while considering interactions among life forms.Exploratory analysis of data by means of principal component analysis(PCA)demonstrates that the highest source of variability along the process is due to water loss,though differences among communities can be observed as well.Indeed,the generation of false color RGB score maps enables the evaluation of different life forms'responses,giving an initial understanding of facilitation and competition mechanisms based on community composition.Moreover,the use of multivariate regression using partial least squares(PLS)regression to predict water content at a pixel level,with a final error in prediction around 3%,leads to the visualization of maps representing the WC of each pixel composing the sample,permitting the evaluation of communities'response at a detailed scale,providing a valuable method for recovering spatial information while monitoring dehydration.The analytical impact and novelty of the approach are supported by the consistency in results obtained from developing the model with two different strategies,image-based and pixel-based,and by the complementarity of the information obtained by the two strategies themselves.展开更多
[Objective] The aims were to explore the relationship between the contents of fruit mineral elements and quality features of the ＇Fuji＇ apple, screen major mineral elements of the fruit affecting fruit quality featu...[Objective] The aims were to explore the relationship between the contents of fruit mineral elements and quality features of the ＇Fuji＇ apple, screen major mineral elements of the fruit affecting fruit quality features, and set up optimum proposals of fruit mineral elements for good fruit qualities, so as to provide a theoretical basis for the reasonable orchard soil and foliar fertilizer applications to increase fruit quality and reduce the physiological diseases related to the ＇Fuji＇ apple. [Methods] The fruit mineral elements and quality indicators of ＇Fuji＂ apples were in- vestigated and analyzed, which were collected from the 153 commercial apple or- chards of ＂Fuji＇ apple located in 51 counties. The variable importance for projection （VlP） of partial least squares regression （PLS） method was used to analyze the model effect and weight analysis impact of the fruit mineral element contents to fruit quality, screen out major factors of fruit mineral elements influencing the different fruit qualities, and set up the regression equation of the fruit qualities and major fruit mineral elements. Linear programming was used to obtain optimum proposals of the fruit mineral elements to achieve good ＇Fuji＇ apple qualities. [Results] The mineral elements content and quality characteristics in ＂Fuji＇ apple fruit had great differences in the different produce regions in which the maximum content of nitro- gen, iron, zinc and boron in the ＇Fuji＇ fruit were12.06, 6.17, 7.7, and 10.08 times greater than the minimum respectively, and the differences for titratable acid and the SSC/TA ratio were 2.33 and 2.16 times respectively. The correlation analysis between the fruit mineral element contents and qualities showed that the nitrogen content of fruit had a significantly negative correlation with the soluble solid content, SSC/TA ratio and red color area, while the calcium and iron contents in the fruit were in significantly positive correlation with the soluble solid content and SSC/TA ratio. The model effect and weight analysis showed that the content of nitrogen and iron in the fruit had a greater influence on the integral fruit quality, followed by phosphorus, potassium and calcium. The variable importance for projection （VlP） technology of PLS found that the mean fruit weight was primarily affected by nitro- gen, phosphorus and potassium, and the soluble solid was primarily affected by ni- trogen, calcium and iron, while the red color area was primarily affected by nitro- gen, potassium, calcium, iron and zinc. The regression equation between fruit quality and mineral element contents showed that the mean fruit weight had a greater pos- itive effect coefficient with the content of phosphorus and potassium, and a greater negative effect coefficient with the content of nitrogen in the fruit. Moreover, the sol- uble solid had the largest negative effect coefficient with nitrogen and largest posi- tive effect coefficient with calcium and iron in the fruit. [Conclusion] The maximum content of soluble solid and titratable acid were 1.5 times more than the minimum, and nitrogen, iron, zinc and boron were 6 times more than in the ＇Fuji＇ apple fruit in the different produce regions. Therefore, it is a key technological measure to improve the overall qualities of the ＂Fuji＇ apple by decreasing the content of nitrogen, and increasing the contents of iron, phosphorus, potassium and calcium in the fruit.展开更多
The aim of this paper is to offer a statistically sound method to make a precise account of the speed of land degradation and regeneration processes.Most common analyses of land degradation focus instead on the extent...The aim of this paper is to offer a statistically sound method to make a precise account of the speed of land degradation and regeneration processes.Most common analyses of land degradation focus instead on the extent of degraded areas,rather than on the intensity of degradation processes.The study was implemented for the Potential Extent of Desertification in China(PEDC),composed by arid,semi-arid,and dry sub-humid regions and refers to the period 2002 to 2012.The metrics were standard partial regression coefficients from stepwise regressions,fitted using Net Primary Productivity as the dependent variable,and year number and aridity as predictors.The results indicate that:①the extension of degrading lands(292896 km 2 or 9.12%of PEDC)overcomes the area that is recovering(194560 km 2 or 6.06%of PEDC);and②the intensity of degrading trends is lower than that of increasing trends in three land cover types(grassland,desert,and crops)and in two aridity levels(semi-arid and dry sub-humid).Such an outcome might pinpoint restoration policies by the Chinese government,and document a possible case of hysteresis.展开更多
Although several methods are available to study the extent of isolation by distance （IBD） among natural populations, comparatively few exist to detect the presence of sharp genetic breaks in genetic distance dataset...Although several methods are available to study the extent of isolation by distance （IBD） among natural populations, comparatively few exist to detect the presence of sharp genetic breaks in genetic distance datasets. In recent years, Monmonier＇s maximum-difference algorithm has been increasingly used by population geneticists. However, this method does not provide means to measure the statistical significance of such barriers, nor to determine their relative contribution to population differentiation with respect to IBD. Here, we propose an approach to assess the significance of genetic boundaries. The method is based on the calculation of a multiple regression from distance matrices, where binary matrices represent putative genetic barriers to test, in addition to geographic and genetic distances. Simulation results suggest that this method reliably detects the presence of genetic barriers, even in situations where IBD is also significant. We also illustrate the methodology by analyzing previously published datasets. Conclusions about the importance of genetic barriers can be misleading if one does not take into consideration their relative contribution to the overall genetic structure of species.展开更多
基金supported by the projects under the Innovation Team of the Safety Standards and Testing Technology for Agricultural Products of Zhejiang Province, China (Grant No.2010R50028)the National Key Technologies R&D Program of China during the 11th Five-Year Plan Period (Grant No.2006BAK02A18)
文摘Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi166） and wild type （Zhonghua 11） rice. Furthermore, rice lines transformed with protein gene （OsTCTP） and regulation gene （Osmi166） were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.
基金the National Natural Science Foundation of China (41171281, 40701120)the Beijing Nova Program, China (2008B33)
文摘Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperature, and precipitation will affect grain protein contents and these factors usually cannot be monitored accurately by remote sensing data from single image. In this research, the relationships between wheat protein content at maturity and wheat agronomic parameters at different growing stages were analyzed and multi-temporal images of Landsat TM were used to estimate grain protein content by partial least squares regression. Experiment data were acquired in the suburb of Beijing during a 2-yr experiment in the period from 2003 to 2004. Determination coefficient, average deviation of self-modeling, and deviation of cross- validation were employed to assess the estimation accuracy of wheat grain protein content. Their values were 0.88, 1.30%, 3.81% and 0.72, 5.22%, 12.36% for 2003 and 2004, respectively. The research laid an agronomic foundation for GPC （grain protein content） estimation by multi-temporal remote sensing. The results showed that it is feasible to estimate GPC of wheat from multi-temporal remote sensing data in large area.
基金the Hi-Tech Research and Development Program(863)of China(No.2006AA10Z203)the National Scienceand Technology Task Force Project(No.2006BAD10A01),China
文摘Detecting plant health conditions plays a key role in farm pest management and crop protection.In this study,measurement of hyperspectral leaf reflectance in rice crop(Oryzasativa L.)was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae(Helminthosporium oryzae Breda.de Hann)through the wavelength range from 350 to 2500 nm.The percentage of leaf surface lesions was estimated and defined as the disease severity.Statistical methods like multiple stepwise regression,principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level.Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps.The root mean square errors(RMSEs)for training(n=210)and testing(n=53)dataset were 6.5%and 5.8%,respectively.Principal component analysis showed that the first principal component could explain approximately 80%of the variance of the original hyperspectral reflectance.The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3%and 13.9%for the training and testing dataset,respec-tively.Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1%and 2.0%for the training and testing dataset,respectively.Our research demon-strates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.
基金Funded by the Natural Basic Research Program of China under the grant No. 2005CB422207.
文摘To predict the economic loss of crops caused by acid rain,we used partial least squares(PLS) regression to build a model of single dependent variable -the economic loss calculated with the decrease in yield related to the pH value and levels of Ca2+,NH4+,Na+,K+,Mg2+,SO42-,NO3-,and Cl-in acid rain. We selected vegetables which were sensitive to acid rain as the sample crops,and collected 12 groups of data,of which 8 groups were used for modeling and 4 groups for testing. Using the cross validation method to evaluate the performace of this prediction model indicates that the optimum number of principal components was 3,determined by the minimum of prediction residual error sum of squares,and the prediction error of the regression equation ranges from -2.25% to 4.32%. The model predicted that the economic loss of vegetables from acid rain is negatively corrrelated to pH and the concentrations of NH4+,SO42-,NO3-,and Cl-in the rain,and positively correlated to the concentrations of Ca2+,Na+,K+ and Mg2+. The precision of the model may be improved if the non-linearity of original data is addressed.
基金National Natural Science Foundation of China No.40301038
文摘In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares （PLS） regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and influencing factors demonstrate the land use character of rural industrialization and urbanization in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.
基金founded by the National Natural Science Foundation of China(81202283,81473070,81373102 and81202267)Key Grant of Natural Science Foundation of the Jiangsu Higher Education Institutions of China(10KJA330034 and11KJA330001)+1 种基金the Research Fund for the Doctoral Program of Higher Education of China(20113234110002)the Priority Academic Program for the Development of Jiangsu Higher Education Institutions(Public Health and Preventive Medicine)
文摘With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.
基金Supported by National Natural Science Foundation of China (No.50478086)Tianjin Special Scientific Innovation Foundation (No.06FZZDSH00900)
文摘The water distribution system of one residential district in Tianjin is taken as an example to analyze the changes of water quality.Partial least squares(PLS) regression model,in which the turbidity and Fe are regarded as control objectives,is used to establish the statistical model.The experimental results indicate that the PLS regression model has good predicted results of water quality compared with the monitored data.The percentages of absolute relative error(below 15%,20%,30%) are 44.4%,66.7%,100%(turbidity) and 33.3%,44.4%,77.8%(Fe) on the 4th sampling point;77.8%,88.9%,88.9%(turbidity) and 44.4%,55.6%,66.7%(Fe) on the 5th sampling point.
文摘The computer auxiliary partial least squares is introduced to simultaneously determine the contents of Deoxyschizandin, Schisandrin, r-Schisandrin in the extracted solution of wuweizi. Regression analysis of the experimental results shows that the average recovery of each component is all in the range from 98.9% to 110.3% , which means the partial least squares regression spectrophotometry can circumvent the overlappirtg of absorption spectrums of mlulti-components, so that sctisfactory results can be obtained without any scrapple pre-separation.
文摘This paper analyzes the drop transfer process in gas metal arc welding in short-circuit transfer mode （GMAW-S） in order to develop an optimized spatter rate model that can be used on line. According to thermodynamic characters and practical behavior, a complete arcing process is divided into three sub-processes： arc re-ignition, energy output and shorting preparation. Shorting process is then divided as drop spread, bridge sustention and bridge destabilization. Nine process variables and their distribution are analyzed based on welding experiments with high-speed photos and synchronous current and voltage signals. Method of variation coefficient is used to reflect process consistency and to design characteristic parameters. Partial least square regression （PLSR） is utilized to set up spatter rate model because of severe correlativity among the above characteristic parameters. PLSR is a new multivariate statistical analysis method, in which regression modeling, data simplification and relativity analysis are included in a single algorithm. Experiment results show that the regression equation based on PLSR is effective for on-line predicting spatter rate of its corresponding welding condition.
基金Project supported by the Fundamental Research Funds for the Central Universities, China (Grant No. 2019XD-A02)the National Natural Science Foundation of China (Grant Nos. U1636106, 61671087, 61170272, and 92046001)+2 种基金Natural Science Foundation of Beijing Municipality, China (Grant No. 4182006)Technological Special Project of Guizhou Province, China (Grant No. 20183001)the Foundation of Guizhou Provincial Key Laboratory of Public Big Data (Grant Nos. 2018BDKFJJ016 and 2018BDKFJJ018)。
文摘Partial least squares(PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this paper, we present a quantum partial least squares(QPLS) regression algorithm. To solve the high time complexity of the PLS regression, we design a quantum eigenvector search method to speed up principal components and regression parameters construction. Meanwhile, we give a density matrix product method to avoid multiple access to quantum random access memory(QRAM)during building residual matrices. The time and space complexities of the QPLS regression are logarithmic in the independent variable dimension n, the dependent variable dimension w, and the number of variables m. This algorithm achieves exponential speed-ups over the PLS regression on n, m, and w. In addition, the QPLS regression inspires us to explore more potential quantum machine learning applications in future works.
文摘During the course of calculating the rice evapotranspiration using weather factors,we often find that some independent variables have multiple correlation.The phenomena can lead to the traditional multivariate regression model which based on least square method distortion.And the stability of the model will be lost.The model will be built based on partial least square regression in the paper,through applying the idea of main component analyze and typical correlation analyze,the writer picks up some component from original material.Thus,the writer builds up the model of rice evapotranspiration to solve the multiple correlation among the independent variables (some weather factors).At last,the writer analyses the model in some parts,and gains the satisfied result.
基金supported by the National Institute of Forest Sciencethe R&D Program for Forest Science Technology (No.2022458B10-2224-0201) of the Korea Forest Service
文摘Pinus densiflora is a pine species native to the Korean peninsula,and seed orchards have supplied mate-rial needed for afforestation in South Korea.Climate vari-ables affecting seed production have not been identified.The purpose of this study was to determine climate variables that influence annual seed production of two seed orchards using multiple linear regression(MLR),elastic net regres-sion(ENR)and partial least square regression(PLSR)mod-els.The PLSR model included 12 climatic variables from 2003 to 2020 and explained 74.3%of the total variation in seed production.It showed better predictive performance(R2=0.662)than the EN(0.516)and the MLR(0.366)mod-els.Among the 12 climatic variables,July temperature two years prior to seed production and July precipitation after one year had the strongest influence on seed production.The time periods indicated by the two variables corresponded to pollen cone initiation and female gametophyte development.The results will be helpful for developing seed collection plans,selecting new orchard sites with favorable climatic conditions,and investigating the relationships between seed production and climatic factors in related pine species.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R194)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.
基金supported by the 948 Program of the State Forestry Administration (2009-4-43)the National Natura Science Foundation of China (No.30870420)
文摘Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used partial least square regression （PLSR） models to relate forest parameters, i.e. canopy closure density and above ground tree biomass, to Landsat ETM＋ data. The established models were optimized according to the variable importance for projection （VIP） criterion and the bootstrap method, and their performance was compared using several statistical indices. All variables selected by the VIP criterion passed the bootstrap test （p〈0.05）. The simplified models without insignificant variables （VIP 〈1） performed as well as the full model but with less computation time. The relative root mean square error （RMSE%） was 29% for canopy closure density, and 58% for above ground tree biomass. We conclude that PLSR can be an effective method for estimating canopy closure density and above ground biomass.
基金supported by the National Natural Science Funds for Distinguished Young Scholar (70825004)National Natural Science Foundation of China (NSFC) (10731010 and 10628104)+3 种基金the National Basic Research Program (2007CB814902)Creative Research Groups of China (10721101)Leading Academic Discipline Program, the 10th five year plan of 211 Project for Shanghai University of Finance and Economics211 Project for Shanghai University of Financeand Economics (the 3rd phase)
文摘This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） proposed a profile least squares estimator for the parametric component and established its asymptotic normality. We further show that the profile least squares estimator can achieve the law of iterated logarithm. Moreover, we study the estimators of the functions characterizing the non-linear part as well as the error variance. The strong convergence rate and the law of iterated logarithm are derived for them, respectively.
文摘Near infrared （NIR） hyperspectral imaging measurement of sugar content in peach was introauced. NIR spectral images （650～1 000 nm, resolution： 2 nm） of peach samples were captured with developed hyperspectral imaging setup. Partial least square （PLS） regression prediction model was developed to estimate the sugar content in peach; step-wise backward method was utilized to determine optimal wavelength subsets. Experimental results show that the calibration model with optimal wavelength subsets has a correlation coefficient of prediction of 0.97 and a standard error of prediction of 0.19, the prediction accuracy is higher than the calibration model applied over the whole wavelength, which proves that variable selection plays an important role in improving the prediction accuracy of PLS regression model.
基金supported by the University of Genoa as part of the Science and Technologies for the Earth and Environment(STAT)doctoral programFinancial support provided by the Italian Ministry of Universities and Research–MUR(Research Project PRIN 2022 n.20223WBTH8,CUP:D53D23008950006)。
文摘In recent years,nonvascular epiphytic communities have been increasingly subjected to extreme climatic conditions,with heavy rains and prolonged droughts.Therefore,understanding their management of water resources provides insight into their ecosystem-level contributions.However,until now,little has been done to assess this feature at a micro-scale level considering species-species interactions.In this context,this study develops an analytical strategy based on hyperspectral imaging(HSI)and chemometrics to map the water content(WC)of nonvascular epiphytic communities during a dehydration process,while considering interactions among life forms.Exploratory analysis of data by means of principal component analysis(PCA)demonstrates that the highest source of variability along the process is due to water loss,though differences among communities can be observed as well.Indeed,the generation of false color RGB score maps enables the evaluation of different life forms'responses,giving an initial understanding of facilitation and competition mechanisms based on community composition.Moreover,the use of multivariate regression using partial least squares(PLS)regression to predict water content at a pixel level,with a final error in prediction around 3%,leads to the visualization of maps representing the WC of each pixel composing the sample,permitting the evaluation of communities'response at a detailed scale,providing a valuable method for recovering spatial information while monitoring dehydration.The analytical impact and novelty of the approach are supported by the consistency in results obtained from developing the model with two different strategies,image-based and pixel-based,and by the complementarity of the information obtained by the two strategies themselves.
基金Supported by the Special Funds for Forestry Industry Research in Public Welfare(201404720)the National Science and Technology Support Program(2014BAD16B02-3)the Science and Technology Innovation Ability Construction Projects of Beijing Academy of Agriculture and Forestry Science(KJCX20150403)~~
文摘[Objective] The aims were to explore the relationship between the contents of fruit mineral elements and quality features of the ＇Fuji＇ apple, screen major mineral elements of the fruit affecting fruit quality features, and set up optimum proposals of fruit mineral elements for good fruit qualities, so as to provide a theoretical basis for the reasonable orchard soil and foliar fertilizer applications to increase fruit quality and reduce the physiological diseases related to the ＇Fuji＇ apple. [Methods] The fruit mineral elements and quality indicators of ＇Fuji＂ apples were in- vestigated and analyzed, which were collected from the 153 commercial apple or- chards of ＂Fuji＇ apple located in 51 counties. The variable importance for projection （VlP） of partial least squares regression （PLS） method was used to analyze the model effect and weight analysis impact of the fruit mineral element contents to fruit quality, screen out major factors of fruit mineral elements influencing the different fruit qualities, and set up the regression equation of the fruit qualities and major fruit mineral elements. Linear programming was used to obtain optimum proposals of the fruit mineral elements to achieve good ＇Fuji＇ apple qualities. [Results] The mineral elements content and quality characteristics in ＂Fuji＇ apple fruit had great differences in the different produce regions in which the maximum content of nitro- gen, iron, zinc and boron in the ＇Fuji＇ fruit were12.06, 6.17, 7.7, and 10.08 times greater than the minimum respectively, and the differences for titratable acid and the SSC/TA ratio were 2.33 and 2.16 times respectively. The correlation analysis between the fruit mineral element contents and qualities showed that the nitrogen content of fruit had a significantly negative correlation with the soluble solid content, SSC/TA ratio and red color area, while the calcium and iron contents in the fruit were in significantly positive correlation with the soluble solid content and SSC/TA ratio. The model effect and weight analysis showed that the content of nitrogen and iron in the fruit had a greater influence on the integral fruit quality, followed by phosphorus, potassium and calcium. The variable importance for projection （VlP） technology of PLS found that the mean fruit weight was primarily affected by nitro- gen, phosphorus and potassium, and the soluble solid was primarily affected by ni- trogen, calcium and iron, while the red color area was primarily affected by nitro- gen, potassium, calcium, iron and zinc. The regression equation between fruit quality and mineral element contents showed that the mean fruit weight had a greater pos- itive effect coefficient with the content of phosphorus and potassium, and a greater negative effect coefficient with the content of nitrogen in the fruit. Moreover, the sol- uble solid had the largest negative effect coefficient with nitrogen and largest posi- tive effect coefficient with calcium and iron in the fruit. [Conclusion] The maximum content of soluble solid and titratable acid were 1.5 times more than the minimum, and nitrogen, iron, zinc and boron were 6 times more than in the ＇Fuji＇ apple fruit in the different produce regions. Therefore, it is a key technological measure to improve the overall qualities of the ＂Fuji＇ apple by decreasing the content of nitrogen, and increasing the contents of iron, phosphorus, potassium and calcium in the fruit.
基金European Space Agency(No.4000123342/18/I-NB)Science and Technology Service Network Initiative of Chinese Academy of Sciences(No.KFJ-STSZDTP-010-02)。
文摘The aim of this paper is to offer a statistically sound method to make a precise account of the speed of land degradation and regeneration processes.Most common analyses of land degradation focus instead on the extent of degraded areas,rather than on the intensity of degradation processes.The study was implemented for the Potential Extent of Desertification in China(PEDC),composed by arid,semi-arid,and dry sub-humid regions and refers to the period 2002 to 2012.The metrics were standard partial regression coefficients from stepwise regressions,fitted using Net Primary Productivity as the dependent variable,and year number and aridity as predictors.The results indicate that:①the extension of degrading lands(292896 km 2 or 9.12%of PEDC)overcomes the area that is recovering(194560 km 2 or 6.06%of PEDC);and②the intensity of degrading trends is lower than that of increasing trends in three land cover types(grassland,desert,and crops)and in two aridity levels(semi-arid and dry sub-humid).Such an outcome might pinpoint restoration policies by the Chinese government,and document a possible case of hysteresis.
基金supported by a Natural Sciences and Engineering Research Council of Canada scholarship and a Fonds Québécois de la Recherche sur la Nature et les Technologies scholarship to S.R.P.a Natural Sciences and Engineering Research Council of Canada grant to F.-J.L.
文摘Although several methods are available to study the extent of isolation by distance （IBD） among natural populations, comparatively few exist to detect the presence of sharp genetic breaks in genetic distance datasets. In recent years, Monmonier＇s maximum-difference algorithm has been increasingly used by population geneticists. However, this method does not provide means to measure the statistical significance of such barriers, nor to determine their relative contribution to population differentiation with respect to IBD. Here, we propose an approach to assess the significance of genetic boundaries. The method is based on the calculation of a multiple regression from distance matrices, where binary matrices represent putative genetic barriers to test, in addition to geographic and genetic distances. Simulation results suggest that this method reliably detects the presence of genetic barriers, even in situations where IBD is also significant. We also illustrate the methodology by analyzing previously published datasets. Conclusions about the importance of genetic barriers can be misleading if one does not take into consideration their relative contribution to the overall genetic structure of species.
