In this research, the result of the cloud seeding over Yazd province during three months of February, March and April in 1999 has been evaluated using the historical regression method. Hereupon, the rain-gages in Yazd...In this research, the result of the cloud seeding over Yazd province during three months of February, March and April in 1999 has been evaluated using the historical regression method. Hereupon, the rain-gages in Yazd province as the target stations and the rain-gages of the neighboring provinces as the control stations have been selected. The rainfall averages for the three aforementioned months through 25 years (1973-1997) in all control and target stations have been calculated. In the next step, the correlations between the rainfalls of control and target stations have been estimated about 75%, which indicates a good consistency in order to use the historical regression. Then, through the obtained liner correlation equation between the control and target stations the precipitation amount for February, March and April in 1999, over the target region (Yazd province) was estimated about 27.57 mm, whiles the observed amount was 34.23 mm. In fact the precipitation increasing around 19.5% over Yazd province confirmed the success of this cloud seeding project.展开更多
According to the appearing of isosbestic point in the absorption spectra of Ho/Y-Tribromoarsenazo (TBA)systems,the complexation reaction is considered to be M+nL=ML_n.A method has been proposed based on it for calcula...According to the appearing of isosbestic point in the absorption spectra of Ho/Y-Tribromoarsenazo (TBA)systems,the complexation reaction is considered to be M+nL=ML_n.A method has been proposed based on it for calculating the mole fraction of free complexing agent in the solutions from spectral data.and two linear regression formula have been introduced to determine the composition,the molar absorptivity,the conditional stability constant of the complex and the concentration of the complexing agent. This method has been used in Ho-TBA and Y-TBA systems.Ho^(3+)and Y^(3+)react with TBA and form 1: 2 complexes in HCl-NaAc buffer solution at pH 3.80.Their molar absorptivities determined are 1.03×10~8 and 1.10×10~8 cm^2·mol^(-1),and the conditional stability constants(logβ_2)are 11.37 and 11.15 respectively.After considering the pH effect in TBA complexing,their stability constants(log β_2^(ahs))are 43.23 and 43.01. respectively.The new method is adaptable to such systems where the accurate concentration of the complexing agent can not be known conveniently.展开更多
A new method,dual-series linear regression method,has been used to study the complexation equilibrium of praseodymium(Pr^(3+))with tribromoarsenazo(TBA)without knowing the accurate concentra- tion of the complexing ag...A new method,dual-series linear regression method,has been used to study the complexation equilibrium of praseodymium(Pr^(3+))with tribromoarsenazo(TBA)without knowing the accurate concentra- tion of the complexing agent TBA.In 1.2 mol/L HCl solution, Pr^(3+)reacts with TBA and forms 1:3 com- plex,the conditional stability constant(lgβ_3)of the complex determined is 15.47,and its molar absorptivity(ε_3^(630))is 1.48×10~5 L·mol^(-1)·cm^(-1).展开更多
The fast spread of coronavirus disease(COVID-19)caused by SARSCoV-2 has become a pandemic and a serious threat to the world.As of May 30,2020,this disease had infected more than 6 million people globally,with hundreds...The fast spread of coronavirus disease(COVID-19)caused by SARSCoV-2 has become a pandemic and a serious threat to the world.As of May 30,2020,this disease had infected more than 6 million people globally,with hundreds of thousands of deaths.Therefore,there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems.This study uses gradient boosting regression(GBR)to build a trained model to predict the daily total confirmed cases of COVID-19.The GBR method can minimize the loss function of the training process and create a single strong learner from weak learners.Experiments are conducted on a dataset of daily confirmed COVID-19 cases from January 22,2020,to May 30,2020.The results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR method.The results reveal that the GBR model achieves 0.00686 root mean square error,the lowest among several comparative models.展开更多
Using two nuclear models,i)the relativistic continuum Hartree-Bogoliubov(RCHB)theory and ii)the Weizsäcker-Skyrme(WS)model WS*,the performances of nine kinds of kernel functions in the kernel ridge regression(KRR...Using two nuclear models,i)the relativistic continuum Hartree-Bogoliubov(RCHB)theory and ii)the Weizsäcker-Skyrme(WS)model WS*,the performances of nine kinds of kernel functions in the kernel ridge regression(KRR)method are investigated by comparing the accuracies of describing the experimental nuclear charge radii and the extrapolation abilities.It is found that,except the inverse power kernel,all other kernels can reach the same level around 0.015~0.016 fm for these two models with KRR method.The extrapolation ability for the neutron rich region of each kernel depends on the trainning data.Our investigation shows that the performances of the power kernel and Multiquadric kernel are better in the RCHB+KRR calculation,and the Gaussian kernel is better in the WS*+KRR calculation.In addition,the performance of different basis functions in the radial basis function method is also investigated for comparison.The results are similar to the KRR method.The influence of different kernels on the KRR reconstruct function is discussed by investigating the whole nuclear chart.At last,the charge radii of some specific isotopic chains have been investigated by the RCHB+KRR with power kernel and the WS*+KRR with Gaussian kernel.The charge radii and most of the specific features in these isotopic chains can be reproduced after considering the KRR method.展开更多
Due to the heterogeneity of rock masses and the variability of in situ stress,the traditional linear inversion method is insufficiently accurate to achieve high accuracy of the in situ stress field.To address this cha...Due to the heterogeneity of rock masses and the variability of in situ stress,the traditional linear inversion method is insufficiently accurate to achieve high accuracy of the in situ stress field.To address this challenge,nonlinear stress boundaries for a numerical model are determined through regression analysis of a series of nonlinear coefficient matrices,which are derived from the bubbling method.Considering the randomness and flexibility of the bubbling method,a parametric study is conducted to determine recommended ranges for these parameters,including the standard deviation(σb)of bubble radii,the non-uniform coefficient matrix number(λ)for nonlinear stress boundaries,and the number(m)and positions of in situ stress measurement points.A model case study provides a reference for the selection of these parameters.Additionally,when the nonlinear in situ stress inversion method is employed,stress distortion inevitably occurs near model boundaries,aligning with the Saint Venant's principle.Two strategies are proposed accordingly:employing a systematic reduction of nonlinear coefficients to achieve high inversion accuracy while minimizing significant stress distortion,and excluding regions with severe stress distortion near the model edges while utilizing the central part of the model for subsequent simulations.These two strategies have been successfully implemented in the nonlinear in situ stress inversion of the Xincheng Gold Mine and have achieved higher inversion accuracy than the linear method.Specifically,the linear and nonlinear inversion methods yield root mean square errors(RMSE)of 4.15 and 3.2,and inversion relative errors(δAve)of 22.08%and 17.55%,respectively.Therefore,the nonlinear inversion method outperforms the traditional multiple linear regression method,even in the presence of a systematic reduction in the nonlinear stress boundaries.展开更多
The extended kernel ridge regression(EKRR)method with odd-even effects was adopted to improve the description of the nuclear charge radius using five commonly used nuclear models.These are:(i)the isospin-dependent A^(...The extended kernel ridge regression(EKRR)method with odd-even effects was adopted to improve the description of the nuclear charge radius using five commonly used nuclear models.These are:(i)the isospin-dependent A^(1∕3) formula,(ii)relativistic continuum Hartree-Bogoliubov(RCHB)theory,(iii)Hartree-Fock-Bogoliubov(HFB)model HFB25,(iv)the Weizsacker-Skyrme(WS)model WS*,and(v)HFB25*model.In the last two models,the charge radii were calculated using a five-parameter formula with the nuclear shell corrections and deformations obtained from the WS and HFB25 models,respectively.For each model,the resultant root-mean-square deviation for the 1014 nuclei with proton number Z≥8 can be significantly reduced to 0.009-0.013 fm after considering the modification with the EKRR method.The best among them was the RCHB model,with a root-mean-square deviation of 0.0092 fm.The extrapolation abilities of the KRR and EKRR methods for the neutron-rich region were examined,and it was found that after considering the odd-even effects,the extrapolation power was improved compared with that of the original KRR method.The strong odd-even staggering of nuclear charge radii of Ca and Cu isotopes and the abrupt kinks across the neutron N=126 and 82 shell closures were also calculated and could be reproduced quite well by calculations using the EKRR method.展开更多
The objective of this study was to compare the energy values of high-fiber dietary ingredients with different solubility(sugar beet pulp[SBP]and defatted rice bran[DFRB])in growing pigs using the difference and the re...The objective of this study was to compare the energy values of high-fiber dietary ingredients with different solubility(sugar beet pulp[SBP]and defatted rice bran[DFRB])in growing pigs using the difference and the regression methods.A total of 21 barrows(initial BW,40.5±1.2 kg)were assigned to 3 blocks with BW as a blocking factor,and each block was assigned to a 7×2 incomplete Latin square design with 7 diets and two 13-d experimental periods.The 7 experimental diets consisted of a corn-soybean meal basal diet and 6 additional diets containing 10%,20%,or 30%SBP or DFRB in the basal diet,respectively.Each of the exper-imental periods lasted 12 d,with a 7 d dietary adaptation period followed by 5-d total fecal and urine collection.Results showed that the digestible energy(DE)and metabolizable energy(ME)of the SBP determined by the difference method with different inclusion levels(10%,20%,or 30%)were 2,712 and 2,628 kcal/kg,2,683 and 2,580 kcal/kg,and 2,643 and 2,554 kcal/kg DM basis,respectively.The DE and ME in the DFRB evaluated by the difference method with 3 different inclusion levels were 2,407 and 2,243 kcal/kg,2,687 and 2,598 kcal/kg,and 2,630 and 2,544 kcal/kg DM basis,respectively.Different inclusion levels had no effects on the energy values of each test ingredient estimated by the difference method.The DE and ME of the SBP and the DFRB estimated by the regression method were 2,562 and 2,472 kcal/kg and 2,685 and 2,606 kcal/kg DM basis,respectively.The energy values of each ingredient determined by the regression method were similar to the values estimated by the difference method with the 20%or 30%inclusion level.However,the energy values of the SBP and DFRB estimated by the difference method with the 10%inclusion level were inconsistent with the values determined by the regression method(P<0.05).In conclusion,the regression method was a robust indirect method to evaluate the energy values for high-fiber ingredients with different solubility in growing pigs.If the number of experimental animals was limited,the difference method with a moderate inclusion level(at least 20%)of the test high-fiber ingredient in the basal diet could be applied to substitute the regression method.展开更多
In order to explore the nonlinear structure hidden in high-dimensional data, some dimen-sion reduction techniques have been developed, such as the Projection Pursuit technique (PP).However, PP will involve enormous co...In order to explore the nonlinear structure hidden in high-dimensional data, some dimen-sion reduction techniques have been developed, such as the Projection Pursuit technique (PP).However, PP will involve enormous computational load. To overcome this, an inverse regressionmethod is proposed. In this paper, we discuss and develop this method. To seek the interestingprojective direction, the minimization of the residual sum of squares is used as a criterion, andspline functions are applied to approximate the general nonlinear transform function. The algo-rithm is simple, and saves the computational load. Under certain proper conditions, consistencyof the estimators of the interesting direction is shown.展开更多
Cable-stayed bridges have been widely used in high-speed railway infrastructure.The accurate determination of cable’s representative temperatures is vital during the intricate processes of design,construction,and mai...Cable-stayed bridges have been widely used in high-speed railway infrastructure.The accurate determination of cable’s representative temperatures is vital during the intricate processes of design,construction,and maintenance of cable-stayed bridges.However,the representative temperatures of stayed cables are not specified in the existing design codes.To address this issue,this study investigates the distribution of the cable temperature and determinates its representative temperature.First,an experimental investigation,spanning over a period of one year,was carried out near the bridge site to obtain the temperature data.According to the statistical analysis of the measured data,it reveals that the temperature distribution is generally uniform along the cable cross-section without significant temperature gradient.Then,based on the limited data,the Monte Carlo,the gradient boosted regression trees(GBRT),and univariate linear regression(ULR)methods are employed to predict the cable’s representative temperature throughout the service life.These methods effectively overcome the limitations of insufficient monitoring data and accurately predict the representative temperature of the cables.However,each method has its own advantages and limitations in terms of applicability and accuracy.A comprehensive evaluation of the performance of these methods is conducted,and practical recommendations are provided for their application.The proposed methods and representative temperatures provide a good basis for the operation and maintenance of in-service long-span cable-stayed bridges.展开更多
Measurement of soil bulk density is important for understanding the physical, chemical, and biological properties of soil. Accurate and rapid soil bulk density measurement techniques play a significant role in agricul...Measurement of soil bulk density is important for understanding the physical, chemical, and biological properties of soil. Accurate and rapid soil bulk density measurement techniques play a significant role in agricultural experimental research. This review is a comprehensive summary of existing measurement methods and evaluates their advantages, disadvantages, potential sources of error,and directions for future development. These techniques can be broadly categorised as direct and indirect methods. Direct methods include core, clod, and excavation sampling, whereas indirect methods include the radiation and regression approaches. The core method is most widely used, but it is time consuming and difficult to use for sampling multiple soil depths. The size of the coring cylinder used, operator experience, sampling depth, and in-situ soil moisture content significantly affect its accuracy. The clod method is suitable for use with heavy clay soils, and its accuracy is dependent on equipment calibration, drying time, and operator experience, but the process is complicated and time consuming. Excavation techniques are most commonly used to evaluate the bulk density of forest soils, but have major limitations as they cannot be used in soils with large pores and their measurement accuracy is strongly influenced by soil texture and the type of analysis selected. The indirect methods appear to have greater accuracy than direct approaches, but have higher costs, are more complex, and require greater operator experience. One such approach uses gamma radiation, and its accuracy is strongly influenced by soil depth. Regression methods are economical as they can make indirect measurements, but these depend on good, quality data of soil texture and organic matter content and geographical and climatic properties. Also, like most of the other approaches, its accuracy decreases with sampling depth.展开更多
Recent technological advancements and developments have led to a dramatic increase in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regression methods.Num...Recent technological advancements and developments have led to a dramatic increase in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regression methods.Numerous traditional multivariate approaches such as principal component analysis have been used broadly in various research areas,including investment analysis,image identification,and population genetic structure analysis.However,these common approaches have the limitations of ignoring the correlations between responses and a low variable selection efficiency.Therefore,in this article,we introduce the reduced rank regression method and its extensions,sparse reduced rank regression and subspace assisted regression with row sparsity,which hold potential to meet the above demands and thus improve the interpretability of regression models.We conducted a simulation study to evaluate their performance and compared them with several other variable selection methods.For different application scenarios,we also provide selection suggestions based on predictive ability and variable selection accuracy.Finally,to demonstrate the practical value of these methods in the field of microbiome research,we applied our chosen method to real population-level microbiome data,the results of which validated our method.Our method extensions provide valuable guidelines for future omics research,especially with respect to multivariate regression,and could pave the way for novel discoveries in microbiome and related research fields.展开更多
A Mobile Ad-hoc NETwork(MANET)contains numerous mobile nodes,and it forms a structure-less network associated with wireless links.But,the node movement is the key feature of MANETs;hence,the quick action of the nodes ...A Mobile Ad-hoc NETwork(MANET)contains numerous mobile nodes,and it forms a structure-less network associated with wireless links.But,the node movement is the key feature of MANETs;hence,the quick action of the nodes guides a link failure.This link failure creates more data packet drops that can cause a long time delay.As a result,measuring accurate link failure time is the key factor in the MANET.This paper presents a Fuzzy Linear Regression Method to measure Link Failure(FLRLF)and provide an optimal route in the MANET-Internet of Things(IoT).This work aims to predict link failure and improve routing efficiency in MANET.The Fuzzy Linear Regression Method(FLRM)measures the long lifespan link based on the link failure.The mobile node group is built by the Received Signal Strength(RSS).The Hill Climbing(HC)method selects the Group Leader(GL)based on node mobility,node degree and node energy.Additionally,it uses a Data Gathering node forward the infor-mation from GL to the sink node through multiple GL.The GL is identified by linking lifespan and energy using the Particle Swarm Optimization(PSO)algo-rithm.The simulation results demonstrate that the FLRLF approach increases the GL lifespan and minimizes the link failure time in the MANET.展开更多
Low levels of environmental education,energy consumption,and other anthropogenic factors strongly contribute to the rising temperature in the world's atmosphere.As such,this study reveals how energy consumption an...Low levels of environmental education,energy consumption,and other anthropogenic factors strongly contribute to the rising temperature in the world's atmosphere.As such,this study reveals how energy consumption and education affect the ecological footprint(EF)and also determines the education thresholds for EF sustainability in sub-Saharan Africa(SSA).The estimation methods in this study are strictly second-generation econometric techniques because of the problems of slope heterogeneity and cross-sectional dependence discovered in the preliminary analysis.The results confirm cointegration,warranting the need for long-run parameter estimators.The Augment Mean Group estimator suggests that natural resources,non-renewable energy consumption(NRE),and economic growth increase the EF.Although renewable energy consumption(REN)and globalization reduce the EF,these indicators are insignificant.The results of the Method of Moment Quantile Regression(MMQR)reveal that REN exacts an indirect effect on the EF via education.Furthermore,the education thresholds required for ecological sustainability have been established.In line with these outcomes,it is proposed that the region redesign its energy policy to encourage eco-friendly consumption by leaning more on pro-environmental strategies and tightening environmental regulations.展开更多
The difficulties associated with performing direct compression strength tests on rocks lead to the development of indirect test methods for the rock strength assessment. Indirect test methods are simple, more economic...The difficulties associated with performing direct compression strength tests on rocks lead to the development of indirect test methods for the rock strength assessment. Indirect test methods are simple, more economical, less time-consuming, and easily adaptable to the field. The main aim of this study was to derive correlations between direct and indirect test methods for basalt and rhyolite rock types from Carlin trend deposits in Nevada. In the destructive methods, point load index, block punch index, and splitting tensile strength tests are performed. In the non-destructive methods, Schmidt hammer and ultrasonic pulse velocity tests are performed. Correlations between the direct and indirect compression strength tests are developed using linear and nonlinear regression analysis methods. The results show that the splitting tensile strength has the best correlation with the uniaxial compression strength.Furthermore, the Poisson's ratio has no correlation with any of the direct and indirect test results.展开更多
OBJECTIVE:To optimize the vinegar-steaming process of Wuweizi(Fructus Schisandrae Chinensis)using the response surface method(RSM)based on the Box-Behnken design.METHODS:A regression model was constructed with the res...OBJECTIVE:To optimize the vinegar-steaming process of Wuweizi(Fructus Schisandrae Chinensis)using the response surface method(RSM)based on the Box-Behnken design.METHODS:A regression model was constructed with the response variables,the content of Deoxyschizandrin,and the three explanatory factors:length of steaming time,the quantity of vinegar and length of moistening time to evaluate the effects on the processing of Wuweizi(Fructus SchisandraeChinensis).RESULTS:There was a linear relationship between the content of Deoxyschizandrin and the three explanatory factors.When the steaming time was5.49 h,with 2.365 g of vinegar added and a moistening time of 4.13 h,the content of Deoxyschizandrin reached the maximum predicted value of0.1076%,and under the conditions the average content of Deoxyschizandrin was 0.1058%.CONCLUSION:The correlation coefficient of thenonlinear mathematical model was relatively high and the model matched the data well,potentially providing a method for the study of the steaming process.展开更多
Whether environmental regulation can increase employment is still controversial in academic circles around the world.An important reason lies in the validity of an empirical method.Using China’s inter-provincial pane...Whether environmental regulation can increase employment is still controversial in academic circles around the world.An important reason lies in the validity of an empirical method.Using China’s inter-provincial panel data from 2003 to 2015 and the synthetic control method(SCM),this paper focuses on a test that was carried out on the basis of a quasi-natural experiment of the 2007 Emission Trading Pilot(ETP)policy.The test results show that the ETP policy has increased the average employment level by 3.25 percentage points and passed a robustness test.The robustness test using the regression control method(RCM)shows that the average employment level has risen by 3.21 percentage points.This means that the ETP policy has significantly increased employment.The paper also puts forward three policy recommendations:optimizing the trading system for emissions rights,encouraging companies to carry out cleaner production and innovation,and incorporating environmental performance assessments.展开更多
A computational and test method for calibrating the flight loads carried by aircraft wings is proposed.The wing load is measured in real-time based on the resistance and fiber Bragg grating strain gauges.The linear st...A computational and test method for calibrating the flight loads carried by aircraft wings is proposed.The wing load is measured in real-time based on the resistance and fiber Bragg grating strain gauges.The linear stepwise regression method is used to construct the load equations.The mean impact value algorithm is employed to select suitable bridges.In the ground calibration experiment,the wing load calculation equations in both forward and reverse installation states are calibrated.The correctness of the load equations was verified through equation error and inspection error analysis.Finally,the actual flight load of the wing was obtained through flight tests.展开更多
Machine learning methods have advantages in predicting excavation-induced lateral wall displacements.Due to lack of sufficient field data,training data for prediction models were often derived from the results of nume...Machine learning methods have advantages in predicting excavation-induced lateral wall displacements.Due to lack of sufficient field data,training data for prediction models were often derived from the results of numerical simulations,leading to poor prediction accuracy.Based on a specific quantity of data,a multivariate adaptive regression splines method(MARS)was introduced to predict lateral wall deflections caused by deep excavations in thick water-rich sands.Sensitivity of lateral wall deflections to affecting factors was analyzed.It is disclosed that dewatering mode has the most significant influence on lateral wall deflections,while the soil cohesion has the least influence.Using crossvalidation analysis,weights were introduced to modify the MARS method to optimize the prediction model.Comparison of the predicted and measured deflections shows that the prediction based on the modified multivariate adaptive regression splines method(MMARS)is more accurate than that based on the traditional MARS method.The prediction model established in this paper can help engineers make predictions for wall displacement,and the proposed methodology can also serve as a reference for researchers to develop prediction models.展开更多
It is known that the exploitation of opencast coal mines has seriously damaged the environments in the semi-arid areas.Vegetation status can reliably reflect the ecological degeneration and restoration in the opencast...It is known that the exploitation of opencast coal mines has seriously damaged the environments in the semi-arid areas.Vegetation status can reliably reflect the ecological degeneration and restoration in the opencast mining areas in the semi-arid areas.Long-time series MODIS NDVI data are widely used to simulate the vegetation cover to reflect the disturbance and restoration of local ecosystems.In this study, both qualitative(linear regression method and coefficient of variation(CoV)) and quantitative(spatial buffer analysis, and change amplitude and the rate of change in the average NDVI) analyses were conducted to analyze the spatio-temporal dynamics of vegetation during 2000–2017 in Jungar Banner of Inner Mongolia Autonomous Region, China, at the large(Jungar Banner and three mine groups) and small(three types of functional areas: opencast coal mining excavation areas, reclamation areas and natural areas) scales.The results show that the rates of change in the average NDVI in the reclamation areas(20%–60%) and opencast coal mining excavation areas(10%–20%) were considerably higher than that in the natural areas(<7%).The vegetation in the reclamation areas experienced a trend of increase(3–5 a after reclamation)-decrease(the sixth year of reclamation)-stability.The vegetation in Jungar Banner has a spatial heterogeneity under the influences of mining and reclamation activities.The ratio of vegetation improvement area to vegetation degradation area in the west, southwest and east mine groups during 2000–2017 was 8:1, 20:1 and 33:1, respectively.The regions with the high CoV of NDVI above 0.45 were mainly distributed around the opencast coal mining excavation areas, and the regions with the CoV of NDVI above 0.25 were mostly located in areas with low(28.8%) and medium-low(10.2%) vegetation cover.The average disturbance distances of mining activities on vegetation in the three mine groups(west, southwest and east) were 800, 800 and 1000 m, respectively.The greater the scale of mining, the farther the disturbance distances of mining activities on vegetation.We conclude that vegetation reclamation will certainly compensate for the negative impacts of opencast coal mining activities on vegetation.Sufficient attention should be paid to the proportional allocation of plant species(herbs and shrubs) in the reclamation areas, and the restored vegetation in these areas needs to be protected for more than 6 a.Then, as the repair time increased, the vegetation condition of the reclamation areas would exceed that of the natural areas.展开更多
文摘In this research, the result of the cloud seeding over Yazd province during three months of February, March and April in 1999 has been evaluated using the historical regression method. Hereupon, the rain-gages in Yazd province as the target stations and the rain-gages of the neighboring provinces as the control stations have been selected. The rainfall averages for the three aforementioned months through 25 years (1973-1997) in all control and target stations have been calculated. In the next step, the correlations between the rainfalls of control and target stations have been estimated about 75%, which indicates a good consistency in order to use the historical regression. Then, through the obtained liner correlation equation between the control and target stations the precipitation amount for February, March and April in 1999, over the target region (Yazd province) was estimated about 27.57 mm, whiles the observed amount was 34.23 mm. In fact the precipitation increasing around 19.5% over Yazd province confirmed the success of this cloud seeding project.
文摘According to the appearing of isosbestic point in the absorption spectra of Ho/Y-Tribromoarsenazo (TBA)systems,the complexation reaction is considered to be M+nL=ML_n.A method has been proposed based on it for calculating the mole fraction of free complexing agent in the solutions from spectral data.and two linear regression formula have been introduced to determine the composition,the molar absorptivity,the conditional stability constant of the complex and the concentration of the complexing agent. This method has been used in Ho-TBA and Y-TBA systems.Ho^(3+)and Y^(3+)react with TBA and form 1: 2 complexes in HCl-NaAc buffer solution at pH 3.80.Their molar absorptivities determined are 1.03×10~8 and 1.10×10~8 cm^2·mol^(-1),and the conditional stability constants(logβ_2)are 11.37 and 11.15 respectively.After considering the pH effect in TBA complexing,their stability constants(log β_2^(ahs))are 43.23 and 43.01. respectively.The new method is adaptable to such systems where the accurate concentration of the complexing agent can not be known conveniently.
文摘A new method,dual-series linear regression method,has been used to study the complexation equilibrium of praseodymium(Pr^(3+))with tribromoarsenazo(TBA)without knowing the accurate concentra- tion of the complexing agent TBA.In 1.2 mol/L HCl solution, Pr^(3+)reacts with TBA and forms 1:3 com- plex,the conditional stability constant(lgβ_3)of the complex determined is 15.47,and its molar absorptivity(ε_3^(630))is 1.48×10~5 L·mol^(-1)·cm^(-1).
基金The financial support provided from the Deanship of Scientific Research at King SaudUniversity,Research group No.RG-1441-502.
文摘The fast spread of coronavirus disease(COVID-19)caused by SARSCoV-2 has become a pandemic and a serious threat to the world.As of May 30,2020,this disease had infected more than 6 million people globally,with hundreds of thousands of deaths.Therefore,there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems.This study uses gradient boosting regression(GBR)to build a trained model to predict the daily total confirmed cases of COVID-19.The GBR method can minimize the loss function of the training process and create a single strong learner from weak learners.Experiments are conducted on a dataset of daily confirmed COVID-19 cases from January 22,2020,to May 30,2020.The results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR method.The results reveal that the GBR model achieves 0.00686 root mean square error,the lowest among several comparative models.
文摘Using two nuclear models,i)the relativistic continuum Hartree-Bogoliubov(RCHB)theory and ii)the Weizsäcker-Skyrme(WS)model WS*,the performances of nine kinds of kernel functions in the kernel ridge regression(KRR)method are investigated by comparing the accuracies of describing the experimental nuclear charge radii and the extrapolation abilities.It is found that,except the inverse power kernel,all other kernels can reach the same level around 0.015~0.016 fm for these two models with KRR method.The extrapolation ability for the neutron rich region of each kernel depends on the trainning data.Our investigation shows that the performances of the power kernel and Multiquadric kernel are better in the RCHB+KRR calculation,and the Gaussian kernel is better in the WS*+KRR calculation.In addition,the performance of different basis functions in the radial basis function method is also investigated for comparison.The results are similar to the KRR method.The influence of different kernels on the KRR reconstruct function is discussed by investigating the whole nuclear chart.At last,the charge radii of some specific isotopic chains have been investigated by the RCHB+KRR with power kernel and the WS*+KRR with Gaussian kernel.The charge radii and most of the specific features in these isotopic chains can be reproduced after considering the KRR method.
基金funded by the National Key R&D Program of China(Grant No.2022YFC2903904)the National Natural Science Foundation of China(Grant Nos.51904057 and U1906208).
文摘Due to the heterogeneity of rock masses and the variability of in situ stress,the traditional linear inversion method is insufficiently accurate to achieve high accuracy of the in situ stress field.To address this challenge,nonlinear stress boundaries for a numerical model are determined through regression analysis of a series of nonlinear coefficient matrices,which are derived from the bubbling method.Considering the randomness and flexibility of the bubbling method,a parametric study is conducted to determine recommended ranges for these parameters,including the standard deviation(σb)of bubble radii,the non-uniform coefficient matrix number(λ)for nonlinear stress boundaries,and the number(m)and positions of in situ stress measurement points.A model case study provides a reference for the selection of these parameters.Additionally,when the nonlinear in situ stress inversion method is employed,stress distortion inevitably occurs near model boundaries,aligning with the Saint Venant's principle.Two strategies are proposed accordingly:employing a systematic reduction of nonlinear coefficients to achieve high inversion accuracy while minimizing significant stress distortion,and excluding regions with severe stress distortion near the model edges while utilizing the central part of the model for subsequent simulations.These two strategies have been successfully implemented in the nonlinear in situ stress inversion of the Xincheng Gold Mine and have achieved higher inversion accuracy than the linear method.Specifically,the linear and nonlinear inversion methods yield root mean square errors(RMSE)of 4.15 and 3.2,and inversion relative errors(δAve)of 22.08%and 17.55%,respectively.Therefore,the nonlinear inversion method outperforms the traditional multiple linear regression method,even in the presence of a systematic reduction in the nonlinear stress boundaries.
基金This work was supported by the National Natural Science Foundation of China(Nos.11875027,11975096).
文摘The extended kernel ridge regression(EKRR)method with odd-even effects was adopted to improve the description of the nuclear charge radius using five commonly used nuclear models.These are:(i)the isospin-dependent A^(1∕3) formula,(ii)relativistic continuum Hartree-Bogoliubov(RCHB)theory,(iii)Hartree-Fock-Bogoliubov(HFB)model HFB25,(iv)the Weizsacker-Skyrme(WS)model WS*,and(v)HFB25*model.In the last two models,the charge radii were calculated using a five-parameter formula with the nuclear shell corrections and deformations obtained from the WS and HFB25 models,respectively.For each model,the resultant root-mean-square deviation for the 1014 nuclei with proton number Z≥8 can be significantly reduced to 0.009-0.013 fm after considering the modification with the EKRR method.The best among them was the RCHB model,with a root-mean-square deviation of 0.0092 fm.The extrapolation abilities of the KRR and EKRR methods for the neutron-rich region were examined,and it was found that after considering the odd-even effects,the extrapolation power was improved compared with that of the original KRR method.The strong odd-even staggering of nuclear charge radii of Ca and Cu isotopes and the abrupt kinks across the neutron N=126 and 82 shell closures were also calculated and could be reproduced quite well by calculations using the EKRR method.
基金the National Natural Science Foundation of China(31702119)Fundamental Research Funds for the Chinese Academy of Agricultural Sciences(2020-YWF-ZX-04)Agricultural Science and Technology Innovation Program(ASTIP-IAS07)
文摘The objective of this study was to compare the energy values of high-fiber dietary ingredients with different solubility(sugar beet pulp[SBP]and defatted rice bran[DFRB])in growing pigs using the difference and the regression methods.A total of 21 barrows(initial BW,40.5±1.2 kg)were assigned to 3 blocks with BW as a blocking factor,and each block was assigned to a 7×2 incomplete Latin square design with 7 diets and two 13-d experimental periods.The 7 experimental diets consisted of a corn-soybean meal basal diet and 6 additional diets containing 10%,20%,or 30%SBP or DFRB in the basal diet,respectively.Each of the exper-imental periods lasted 12 d,with a 7 d dietary adaptation period followed by 5-d total fecal and urine collection.Results showed that the digestible energy(DE)and metabolizable energy(ME)of the SBP determined by the difference method with different inclusion levels(10%,20%,or 30%)were 2,712 and 2,628 kcal/kg,2,683 and 2,580 kcal/kg,and 2,643 and 2,554 kcal/kg DM basis,respectively.The DE and ME in the DFRB evaluated by the difference method with 3 different inclusion levels were 2,407 and 2,243 kcal/kg,2,687 and 2,598 kcal/kg,and 2,630 and 2,544 kcal/kg DM basis,respectively.Different inclusion levels had no effects on the energy values of each test ingredient estimated by the difference method.The DE and ME of the SBP and the DFRB estimated by the regression method were 2,562 and 2,472 kcal/kg and 2,685 and 2,606 kcal/kg DM basis,respectively.The energy values of each ingredient determined by the regression method were similar to the values estimated by the difference method with the 20%or 30%inclusion level.However,the energy values of the SBP and DFRB estimated by the difference method with the 10%inclusion level were inconsistent with the values determined by the regression method(P<0.05).In conclusion,the regression method was a robust indirect method to evaluate the energy values for high-fiber ingredients with different solubility in growing pigs.If the number of experimental animals was limited,the difference method with a moderate inclusion level(at least 20%)of the test high-fiber ingredient in the basal diet could be applied to substitute the regression method.
基金This project is supported by the National Natural Science Foundation of China
文摘In order to explore the nonlinear structure hidden in high-dimensional data, some dimen-sion reduction techniques have been developed, such as the Projection Pursuit technique (PP).However, PP will involve enormous computational load. To overcome this, an inverse regressionmethod is proposed. In this paper, we discuss and develop this method. To seek the interestingprojective direction, the minimization of the residual sum of squares is used as a criterion, andspline functions are applied to approximate the general nonlinear transform function. The algo-rithm is simple, and saves the computational load. Under certain proper conditions, consistencyof the estimators of the interesting direction is shown.
基金Project(2017G006-N)supported by the Project of Science and Technology Research and Development Program of China Railway Corporation。
文摘Cable-stayed bridges have been widely used in high-speed railway infrastructure.The accurate determination of cable’s representative temperatures is vital during the intricate processes of design,construction,and maintenance of cable-stayed bridges.However,the representative temperatures of stayed cables are not specified in the existing design codes.To address this issue,this study investigates the distribution of the cable temperature and determinates its representative temperature.First,an experimental investigation,spanning over a period of one year,was carried out near the bridge site to obtain the temperature data.According to the statistical analysis of the measured data,it reveals that the temperature distribution is generally uniform along the cable cross-section without significant temperature gradient.Then,based on the limited data,the Monte Carlo,the gradient boosted regression trees(GBRT),and univariate linear regression(ULR)methods are employed to predict the cable’s representative temperature throughout the service life.These methods effectively overcome the limitations of insufficient monitoring data and accurately predict the representative temperature of the cables.However,each method has its own advantages and limitations in terms of applicability and accuracy.A comprehensive evaluation of the performance of these methods is conducted,and practical recommendations are provided for their application.The proposed methods and representative temperatures provide a good basis for the operation and maintenance of in-service long-span cable-stayed bridges.
基金the Government and Ministry of Higher Education and Scientific Research, Iraq, for providing funding for this study as a scholarship for Ph.D. student for the first author Ahmed Abed Gatea Al-Shammary
文摘Measurement of soil bulk density is important for understanding the physical, chemical, and biological properties of soil. Accurate and rapid soil bulk density measurement techniques play a significant role in agricultural experimental research. This review is a comprehensive summary of existing measurement methods and evaluates their advantages, disadvantages, potential sources of error,and directions for future development. These techniques can be broadly categorised as direct and indirect methods. Direct methods include core, clod, and excavation sampling, whereas indirect methods include the radiation and regression approaches. The core method is most widely used, but it is time consuming and difficult to use for sampling multiple soil depths. The size of the coring cylinder used, operator experience, sampling depth, and in-situ soil moisture content significantly affect its accuracy. The clod method is suitable for use with heavy clay soils, and its accuracy is dependent on equipment calibration, drying time, and operator experience, but the process is complicated and time consuming. Excavation techniques are most commonly used to evaluate the bulk density of forest soils, but have major limitations as they cannot be used in soils with large pores and their measurement accuracy is strongly influenced by soil texture and the type of analysis selected. The indirect methods appear to have greater accuracy than direct approaches, but have higher costs, are more complex, and require greater operator experience. One such approach uses gamma radiation, and its accuracy is strongly influenced by soil depth. Regression methods are economical as they can make indirect measurements, but these depend on good, quality data of soil texture and organic matter content and geographical and climatic properties. Also, like most of the other approaches, its accuracy decreases with sampling depth.
基金the National Key Research and Development Program of China(2018YFC2000500)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB29020000)+1 种基金the National Natural Science Foundation of China(31771481 and 91857101)the Key Research Program of the Chinese Academy of Sciences(KFZD-SW-219),“China Microbiome Initiative.”。
文摘Recent technological advancements and developments have led to a dramatic increase in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regression methods.Numerous traditional multivariate approaches such as principal component analysis have been used broadly in various research areas,including investment analysis,image identification,and population genetic structure analysis.However,these common approaches have the limitations of ignoring the correlations between responses and a low variable selection efficiency.Therefore,in this article,we introduce the reduced rank regression method and its extensions,sparse reduced rank regression and subspace assisted regression with row sparsity,which hold potential to meet the above demands and thus improve the interpretability of regression models.We conducted a simulation study to evaluate their performance and compared them with several other variable selection methods.For different application scenarios,we also provide selection suggestions based on predictive ability and variable selection accuracy.Finally,to demonstrate the practical value of these methods in the field of microbiome research,we applied our chosen method to real population-level microbiome data,the results of which validated our method.Our method extensions provide valuable guidelines for future omics research,especially with respect to multivariate regression,and could pave the way for novel discoveries in microbiome and related research fields.
文摘A Mobile Ad-hoc NETwork(MANET)contains numerous mobile nodes,and it forms a structure-less network associated with wireless links.But,the node movement is the key feature of MANETs;hence,the quick action of the nodes guides a link failure.This link failure creates more data packet drops that can cause a long time delay.As a result,measuring accurate link failure time is the key factor in the MANET.This paper presents a Fuzzy Linear Regression Method to measure Link Failure(FLRLF)and provide an optimal route in the MANET-Internet of Things(IoT).This work aims to predict link failure and improve routing efficiency in MANET.The Fuzzy Linear Regression Method(FLRM)measures the long lifespan link based on the link failure.The mobile node group is built by the Received Signal Strength(RSS).The Hill Climbing(HC)method selects the Group Leader(GL)based on node mobility,node degree and node energy.Additionally,it uses a Data Gathering node forward the infor-mation from GL to the sink node through multiple GL.The GL is identified by linking lifespan and energy using the Particle Swarm Optimization(PSO)algo-rithm.The simulation results demonstrate that the FLRLF approach increases the GL lifespan and minimizes the link failure time in the MANET.
文摘Low levels of environmental education,energy consumption,and other anthropogenic factors strongly contribute to the rising temperature in the world's atmosphere.As such,this study reveals how energy consumption and education affect the ecological footprint(EF)and also determines the education thresholds for EF sustainability in sub-Saharan Africa(SSA).The estimation methods in this study are strictly second-generation econometric techniques because of the problems of slope heterogeneity and cross-sectional dependence discovered in the preliminary analysis.The results confirm cointegration,warranting the need for long-run parameter estimators.The Augment Mean Group estimator suggests that natural resources,non-renewable energy consumption(NRE),and economic growth increase the EF.Although renewable energy consumption(REN)and globalization reduce the EF,these indicators are insignificant.The results of the Method of Moment Quantile Regression(MMQR)reveal that REN exacts an indirect effect on the EF via education.Furthermore,the education thresholds required for ecological sustainability have been established.In line with these outcomes,it is proposed that the region redesign its energy policy to encourage eco-friendly consumption by leaning more on pro-environmental strategies and tightening environmental regulations.
基金CDC/NIOSH for their partial funding of this work
文摘The difficulties associated with performing direct compression strength tests on rocks lead to the development of indirect test methods for the rock strength assessment. Indirect test methods are simple, more economical, less time-consuming, and easily adaptable to the field. The main aim of this study was to derive correlations between direct and indirect test methods for basalt and rhyolite rock types from Carlin trend deposits in Nevada. In the destructive methods, point load index, block punch index, and splitting tensile strength tests are performed. In the non-destructive methods, Schmidt hammer and ultrasonic pulse velocity tests are performed. Correlations between the direct and indirect compression strength tests are developed using linear and nonlinear regression analysis methods. The results show that the splitting tensile strength has the best correlation with the uniaxial compression strength.Furthermore, the Poisson's ratio has no correlation with any of the direct and indirect test results.
基金Supported by Scientific Research Foundation of Health Department of Shaanxi Province(2012D14),China
文摘OBJECTIVE:To optimize the vinegar-steaming process of Wuweizi(Fructus Schisandrae Chinensis)using the response surface method(RSM)based on the Box-Behnken design.METHODS:A regression model was constructed with the response variables,the content of Deoxyschizandrin,and the three explanatory factors:length of steaming time,the quantity of vinegar and length of moistening time to evaluate the effects on the processing of Wuweizi(Fructus SchisandraeChinensis).RESULTS:There was a linear relationship between the content of Deoxyschizandrin and the three explanatory factors.When the steaming time was5.49 h,with 2.365 g of vinegar added and a moistening time of 4.13 h,the content of Deoxyschizandrin reached the maximum predicted value of0.1076%,and under the conditions the average content of Deoxyschizandrin was 0.1058%.CONCLUSION:The correlation coefficient of thenonlinear mathematical model was relatively high and the model matched the data well,potentially providing a method for the study of the steaming process.
文摘Whether environmental regulation can increase employment is still controversial in academic circles around the world.An important reason lies in the validity of an empirical method.Using China’s inter-provincial panel data from 2003 to 2015 and the synthetic control method(SCM),this paper focuses on a test that was carried out on the basis of a quasi-natural experiment of the 2007 Emission Trading Pilot(ETP)policy.The test results show that the ETP policy has increased the average employment level by 3.25 percentage points and passed a robustness test.The robustness test using the regression control method(RCM)shows that the average employment level has risen by 3.21 percentage points.This means that the ETP policy has significantly increased employment.The paper also puts forward three policy recommendations:optimizing the trading system for emissions rights,encouraging companies to carry out cleaner production and innovation,and incorporating environmental performance assessments.
基金supported by the National Natural Science Foundation of China(Grant No.11602237)the Middleaged and Young Teachers’Basic Ability Promotion Project of Guangxi(Grant No.2022KY1070)。
文摘A computational and test method for calibrating the flight loads carried by aircraft wings is proposed.The wing load is measured in real-time based on the resistance and fiber Bragg grating strain gauges.The linear stepwise regression method is used to construct the load equations.The mean impact value algorithm is employed to select suitable bridges.In the ground calibration experiment,the wing load calculation equations in both forward and reverse installation states are calibrated.The correctness of the load equations was verified through equation error and inspection error analysis.Finally,the actual flight load of the wing was obtained through flight tests.
基金Financial support from the National Natural Science Foundation of China(Grant No.42177179)is gratefully acknowledged.
文摘Machine learning methods have advantages in predicting excavation-induced lateral wall displacements.Due to lack of sufficient field data,training data for prediction models were often derived from the results of numerical simulations,leading to poor prediction accuracy.Based on a specific quantity of data,a multivariate adaptive regression splines method(MARS)was introduced to predict lateral wall deflections caused by deep excavations in thick water-rich sands.Sensitivity of lateral wall deflections to affecting factors was analyzed.It is disclosed that dewatering mode has the most significant influence on lateral wall deflections,while the soil cohesion has the least influence.Using crossvalidation analysis,weights were introduced to modify the MARS method to optimize the prediction model.Comparison of the predicted and measured deflections shows that the prediction based on the modified multivariate adaptive regression splines method(MMARS)is more accurate than that based on the traditional MARS method.The prediction model established in this paper can help engineers make predictions for wall displacement,and the proposed methodology can also serve as a reference for researchers to develop prediction models.
基金supported by the National Key Research and Development Program of China (2016YFC0501107)the Project of Ordos Science and Technology Program (2017006)the Special Project of Science and Technology Basic Work of Ministry of Science and Technology of China (2014FY110800)
文摘It is known that the exploitation of opencast coal mines has seriously damaged the environments in the semi-arid areas.Vegetation status can reliably reflect the ecological degeneration and restoration in the opencast mining areas in the semi-arid areas.Long-time series MODIS NDVI data are widely used to simulate the vegetation cover to reflect the disturbance and restoration of local ecosystems.In this study, both qualitative(linear regression method and coefficient of variation(CoV)) and quantitative(spatial buffer analysis, and change amplitude and the rate of change in the average NDVI) analyses were conducted to analyze the spatio-temporal dynamics of vegetation during 2000–2017 in Jungar Banner of Inner Mongolia Autonomous Region, China, at the large(Jungar Banner and three mine groups) and small(three types of functional areas: opencast coal mining excavation areas, reclamation areas and natural areas) scales.The results show that the rates of change in the average NDVI in the reclamation areas(20%–60%) and opencast coal mining excavation areas(10%–20%) were considerably higher than that in the natural areas(<7%).The vegetation in the reclamation areas experienced a trend of increase(3–5 a after reclamation)-decrease(the sixth year of reclamation)-stability.The vegetation in Jungar Banner has a spatial heterogeneity under the influences of mining and reclamation activities.The ratio of vegetation improvement area to vegetation degradation area in the west, southwest and east mine groups during 2000–2017 was 8:1, 20:1 and 33:1, respectively.The regions with the high CoV of NDVI above 0.45 were mainly distributed around the opencast coal mining excavation areas, and the regions with the CoV of NDVI above 0.25 were mostly located in areas with low(28.8%) and medium-low(10.2%) vegetation cover.The average disturbance distances of mining activities on vegetation in the three mine groups(west, southwest and east) were 800, 800 and 1000 m, respectively.The greater the scale of mining, the farther the disturbance distances of mining activities on vegetation.We conclude that vegetation reclamation will certainly compensate for the negative impacts of opencast coal mining activities on vegetation.Sufficient attention should be paid to the proportional allocation of plant species(herbs and shrubs) in the reclamation areas, and the restored vegetation in these areas needs to be protected for more than 6 a.Then, as the repair time increased, the vegetation condition of the reclamation areas would exceed that of the natural areas.
