Because of the difficulty to obtain the traffic flow information of lanes at non-detector intersections in most metropolises of the world,based on the relationships between the lanes of signal-controlled intersections...Because of the difficulty to obtain the traffic flow information of lanes at non-detector intersections in most metropolises of the world,based on the relationships between the lanes of signal-controlled intersections,cluster analysis and stepwise regression are integrated to predict the traffic volume of lanes at non-detector isolated controlled intersections.First cluster analysis is used to cluster the lanes of non-detector isolated signal-controlled intersections and the lanes of all signal-controlled intersections with detectors.Then, by the results of cluster analysis,the traffic volume samples are selected randomly and stepwise regression is used to predict the traffic volume of lanes at non-detector isolated signal-controlled intersections.The method is tested by the traffic volume data of lanes of the road network of Nanjing city.The problem of predicting the traffic volume of lanes at non-detector isolated signal-controlled intersections was resolved and can be widely used in urban traffic flow guidance and urban traffic control in cities without enough intersections equipped with detectors.展开更多
[Objective] The research aimed to study the significant influence factors of the population variations of oriental fruit fly. [Method] Using stepwise regression analysis, the population variations law of oriental frui...[Objective] The research aimed to study the significant influence factors of the population variations of oriental fruit fly. [Method] Using stepwise regression analysis, the population variations law of oriental fruit fly in Jianshui County of Yunnan province and the meteorological factors that caused its occurrence were analyzed. And the regression model was built. Finally, the regression model was tested on the basis of the data in Jianshui County of Yunnan Province during 2004-2006.[Result] The main meteorological factors that influenced the occurrence of oriental fruit fly were relative humidity, the lowest monthly temperature and rainfall. [Conclusion] This study will provide certain reference for the prediction researches on the time, quantity and occurrence peak of oriental fruit fly.展开更多
A statistical analysis was conducted on the feeding behavior of 106 York breeding pigs.Pearson correlation analysis,principal component correlation analysis and multiple stepwise regression equation methods were appli...A statistical analysis was conducted on the feeding behavior of 106 York breeding pigs.Pearson correlation analysis,principal component correlation analysis and multiple stepwise regression equation methods were applied to establish regression equations of the York breeding pigs total feed intake per time and average feed intake per time with corrected fat thickness,feed conversion rate,and corrected daily gain.The results showed that:①there were three peak feed intake periods for the pigs,and the correlation coefficient between the feed intake and the corrected fat thickness of the pigs in the 24 h period was positive or negative,that is,increasing the number of feeding times and the feed intake was not necessarily conducive to the fat thickness accumulation,but the breeding goal of fat thickness could be achieved by controlling the feeding times and feed intake;②the average feed intake of pigs in the 60-90 kg body weight stage was 30%-50%higher than that of the 30-60 kg body weight stage,but the number of feeding times decreased,the peak feeding time was more concentrated,and the feeding duration per time was 3.0 min longer,indicating that as the weight of pigs increased,the feed intake increased significantly;and③the stepwise regression equations and the principal component equations showed that the feeding behavior of York pigs in the 30-90 kg growth stage was not only affected by the feeding time within 24 h,but also by environmental factors such as temperature and humidity.The feeding behavior of York pigs is a complex process of interaction between environmental factors and animal factors.展开更多
In this paper, an overview of an important feature in statistics field has shown： the stepwise multiple linear regression. Likewise, a link between stepwise multiple linear regression and earthquakes localization has...In this paper, an overview of an important feature in statistics field has shown： the stepwise multiple linear regression. Likewise, a link between stepwise multiple linear regression and earthquakes localization has been descripted. Precisely, the aim of this research is showing how stepwise multiple linear regression contributes to solution of earthquakes localization, describing its conditions of use in HYPO71PC, a software devoted to computation of seismic sources’ collocation. This aim is reached treating a concrete case, that is computation of earthquakes localization happening on Mount Vesuvius, Italy.展开更多
The total output value of mutton in Northwestern China has accounted for more than 60%of the total output value of animal husbandry over the years.It can be seen that the mutton industry in Northwest China not only pl...The total output value of mutton in Northwestern China has accounted for more than 60%of the total output value of animal husbandry over the years.It can be seen that the mutton industry in Northwest China not only plays a pivotal role in animal husbandry,but also plays an important role in Chinese agriculture.In this study,based on cost accounting theory,income-related theories and total factor productivity theory,using basic knowledge of statistics and economics,drawing on existing research results at home and abroad,and adopting a combination of qualitative analysis and quantitative analysis of SAS multiple stepwise regression,the changing trends of cost-benefit of mutton sheep breeding in Northwest agricultural and pastoral areas and influencing factors of production costs and production efficiency were investigated,aiming to provide reference for saving mutton sheep feeding material resources,reducing mutton sheep breeding costs,and improving mutton sheep breeding benefits.展开更多
This paper has compared variable selection method for multiple linear regression models that have both relative and non-relative variables in full model when predictor variables are highly correlated 0.999 . In this s...This paper has compared variable selection method for multiple linear regression models that have both relative and non-relative variables in full model when predictor variables are highly correlated 0.999 . In this study two objective functions used in the Tabu Search are mean square error （MSE） and the mean absolute error （MAE）. The results of Tabu Search are compared with the results obtained by stepwise regression method based on the hit percentage criterion. The simulations cover the both cases, without and with multicollinearity problems. For each situation, 1,000 iterations are examined by applying a different sample size n = 25 and 100 at 0.05 level of significance. Without multicollinearity problem, the hit percentages of the stepwise regression method and Tabu Search using the objective function of MSE are almost the same but slightly higher than the Tabu Search using the objective function of MAE. However with multicollinearity problem the hit percentages of the Tabu Search using both objective functions are higher than the hit percentage of the stepwise regression method.展开更多
The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for...The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for modelling urban growth to closely reflects reality.Despite extensive research,ambiguity remains about how variations in these input variables influence urban densification.In this study,we conduct a global sensitivity analysis(SA)using a multinomial logistic regression(MNL)model to assess the model’s explanatory and predictive power.We examine the influence of global variables,including spatial resolution,neighborhood size,and density classes,under different input combinations at a provincial scale to understand their impact on densification.Additionally,we perform a stepwise regression to identify the significant explanatory variables that are important for understanding densification in the Brussels Metropolitan Area(BMA).Our results indicate that a finer spatial resolution of 50 m and 100 m,smaller neighborhood size of 5×5 and 3×3,and specific density classes—namely 3(non-built-up,low and high built-up)and 4(non-built-up,low,medium and high built-up)—optimally explain and predict urban densification.In line with the same,the stepwise regression reveals that models with a coarser resolution of 300 m lack significant variables,reflecting a lower explanatory power for densification.This approach aids in identifying optimal and significant global variables with higher explanatory power for understanding and predicting urban densification.Furthermore,these findings are reproducible in a global urban context,offering valuable insights for planners,modelers and geographers in managing future urban growth and minimizing modelling.展开更多
Accurate information for consumer phase connectivity in a low-voltage distribution network(LVDN)is critical for the management of line losses and the quality of customer service.The wide application of smart meters pr...Accurate information for consumer phase connectivity in a low-voltage distribution network(LVDN)is critical for the management of line losses and the quality of customer service.The wide application of smart meters provides the data basis for the phase identification of LVDN.However,the measurement errors,poor communication,and data distortion have significant impacts on the accuracy of phase identification.In order to solve this problem,this paper proposes a phase identification method of LVDN based on stepwise regression(SR)method.First,a multiple linear regression model based on the principle of energy conservation is established for phase identification of LVDN.Second,the SR algorithm is used to identify the consumer phase connectivity.Third,by defining a significance correction factor,the results from the SR algorithm are updated to improve the accuracy of phase identification.Finally,an LVDN test system with 63 consumers is constructed based on the real load.The simulation results prove that the identification accuracy achieved by the proposed method is higher than other phase identification methods under the influence of various errors.展开更多
The prediction accuracy of the traditional stepwise regression prediction equation（SRPE）is affected by the multicollinearity among its predictors.This paper introduces the condition number analysis into the predicti...The prediction accuracy of the traditional stepwise regression prediction equation（SRPE）is affected by the multicollinearity among its predictors.This paper introduces the condition number analysis into the prediction modeling to minimize the multicollinearity in the SRPE.In the condition number prediction modeling,the condition number is used to select the combination of predictors with the lowest multicollinearity from the possible combinations of a number of candidate predictors（variables）,and the selected combination is then used to construct the condition number regression prediction equation（CNRPE）.This novel prediction modeling is performed in typhoon track prediction,which is a difficult task among meteorological disaster predictions.Six pairs of typhoon track latitude/longitude SRPEs and CNRPEs for July,August,and September are built by employing the traditional and the novel prediction modeling approaches,respectively,and by using a large number of identical modeling samples.The comparative analysis indicates that under the condition of the same candidate predictors（variables）and predictands（dependent variables）,although the fitting accuracy of the novel prediction models used for the historical samples of South China Sea（SCS）typhoon tracks is slightly lower than that of the traditional prediction models,the prediction accuracy for the independent samples is obviously improved,with the averaged prediction error of the novel models for July,August,and September being 153.9 kin,which is 75.3 km smaller than that of the traditional models（a reduction of 33%）.This is because the novel prediction modeling effectively minimizes the multicollinearity by computation and analysis of the condition number.It is shown further that when F=1.0,2.0,and 3.0,the average prediction errors of the traditional SRPEs are obviously larger than those of the CNRPEs.Moreover,extremely large and unreasonable prediction errors occur at some individual points of the typhoon track predicted by the SRPEs due to the multicollinearity existing in the combination of predictors.展开更多
The energy sector in Poland is the source of 81% of greenhouse gas（GHG） emissions. Poland,among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector activ...The energy sector in Poland is the source of 81% of greenhouse gas（GHG） emissions. Poland,among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO2 emissions demonstrate a high relationship（0.97） with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH4 emission, indicated the presence of hard coal（0.66）, peat and fuel wood（0.34）, solid waste fuels, as well as other sources（- 0.64） as the most important variables. The adjusted coefficient is suitable and equals R2= 0.90. For N2 O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N2 O emission is the peat and wood fuel consumption.展开更多
Abstract Using the method of stepwise multivariate linear regression (SMLR), the quantitative structure activity relationships (QSAR) of two isomeric series of taxol and its derivatives have been studied. It was foun...Abstract Using the method of stepwise multivariate linear regression (SMLR), the quantitative structure activity relationships (QSAR) of two isomeric series of taxol and its derivatives have been studied. It was found that the molar refractivity of the C3′substituent of the C13 side chain has significant correlation with its activity. We deduce that structural changes in the C3′substituents may be critical to the anticancer function. It would be useful to the design and synthesis of taxol like compounds with improved activities.展开更多
Aim New statistical method was applied in data analysis of orthogonal experiments to optimize the preparation of liposome. Method Particle size, zeta potential, encapsulation efficiency and physical stability of lipos...Aim New statistical method was applied in data analysis of orthogonal experiments to optimize the preparation of liposome. Method Particle size, zeta potential, encapsulation efficiency and physical stability of liposomes were selected by orthogonal design as evaluating indicators. Through three statistical methods （direct observation, variance analysis and stepwise multiple regression）, the optimized preparing conditions were acquired and validated by experiment. Results All of the four indicators were different by these analyses. The validation experiments indicated that the optimized conditions by stepwise multiple regressions were better than that by traditional analysis. Conclusion Experiment results suggested that multiple regressions could avoid the weakness of direct observation and variance analysis, but more work should be done in preparing liposomes.展开更多
Partial least squares （PLS） regression was applied to the Lunar Soft Characterization Consortium （LSCC） dataset for spectral estimation of TiO2. The LSCC dataset was split into a number of subsets including the lo...Partial least squares （PLS） regression was applied to the Lunar Soft Characterization Consortium （LSCC） dataset for spectral estimation of TiO2. The LSCC dataset was split into a number of subsets including the low-Ti, high-Ti, total mare soils, total highland, Apollo 16, and Apollo 14 soils to investigate the effects of interfering minerals and nonlinearity on the PLS performance. The PLS weight loading vectors were analyzed through stepwise multiple regression analysis （SMRA） to identify mineral species driving and interfering the PLS performance. PLS exhibits high performance for estimating TiO2 for the LSCC low-Ti and high-Ti mare samples and both groups analyzed together. The results suggest that while the dominant TiO2-bearing minerals are few, additional PLS factors are required to compensate the effects on the important PLS factors of minerals that are not highly corrected to TiO2, to accommodate nonlinear relationships between reflectance and TiO2, and to correct inconsistent mineral-TiO2 correlations between the high-Ti and iow-Ti mare samples. Analysis of the LSCC highland soil samples indicates that the Apollo 16 soils are responsible for the large errors of TiO2 estimates when the soils are modeled with other subgroups. For the LSCC Apollo 16 samples, the dominant spectral effects of plagioclase over other dark minerals are primarily responsible for large errors of estimated TiO2. For the Apollo 14 soils, more accurate estimation for TiO2 is attributed to the posi- tive correlation between a major TiOe-bearing component and TiO2, explaining why the Apollo 14 soils follow the regression trend when analyzed with other soils groups.展开更多
In the physical model test of landslides,the selection of analogous materials is the key,and it is difficult to consider the similarity of mechanical properties and seepage performance at the same time.To develop a mo...In the physical model test of landslides,the selection of analogous materials is the key,and it is difficult to consider the similarity of mechanical properties and seepage performance at the same time.To develop a model material suitable for analysing the deformation and failure of reservoir landslides,based on the existing research foundation of analogous materials,5 materials and 5 physical-mechanical parameters were selected to design an orthogonal test.The factor sensitivity of each component ratio and its influence on the physical-mechanical indices were studied by range analysis and stepwise regression analysis,and the proportioning method was determined.Finally,the model material was developed,and a model test was carried out considering Huangtupo as the prototype application.The results showed that(1)the model material composed of sand,barite powder,glass beads,clay,and bentonite had a wide distribution of physical-mechanical parameters,which could be applied to model tests under different conditions;(2)the physical-mechanical parameters of analogous materials matched the application prototype;and(3)the mechanical properties and seepage performance of the model material sample met the requirements of reservoir landslide model tests,which could be used to simulate landslide evolution and analyse the deformation process.展开更多
The Yemaomian landslide,the largest near-dam accumulation landslide in the Three Gorges Reservoir area,is situated 17 km upstream of the Three Gorges Dam.Nearly 20 years of monitoring data indicate that the landslide ...The Yemaomian landslide,the largest near-dam accumulation landslide in the Three Gorges Reservoir area,is situated 17 km upstream of the Three Gorges Dam.Nearly 20 years of monitoring data indicate that the landslide has been undergoing slow deformation with a low deformation rate and magnitude.This paper applies a stepwise linear regression method and a mechanical model of hydrodynamics triggering to deeply explore the relationship between geological conditions,external factors,and deformation characteristics.Based on the stage transition characteristics of external triggering factors,the deformation evolution process of the landslide since the reservoir impoundment is divided into three stages:(1)June 2003-September 2006,the landslide was reactivated by the significant rise in reservoir water levels,in a retrogressive mode;(2)October 2006-September 2018,the deformation mode shifted from retrogressive mode to creep deformation as a whole,primarily due to the degradation effect on the landslide mass caused by immersion in reservoir water.(3)October 2018-February 2024,a further significant reduction in the overall deformation rate and the impact of seasonal rainfall on landslide deformation surpassed that of reservoir water level fluctuations.The main component of landslide deformation is convergent creep,and extreme rainfall will be an important triggering factor for the local instability.Identifying the deformation evolution stages and determining the dominant external influencing factors at each stage is crucial for landslide research,and this paper provides an effective research paradigm for this purpose.展开更多
Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the applica...Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-researched.In this study,stepwise multiple linear regression(SMLR)and Random Forest(RF)were used to evaluate the spatial and temporal variation of NUE indicators(i.e.,partial factor productivity of N(PFPN);partial nutrient balance of N(PNBN))at county scale in Northeast China(Heilongjiang,Liaoning and Jilin provinces)from 1990 to 2015.Explanatory variables included agricultural management practices,topography,climate,economy,soil and crop types.Results revealed that the PFPN was higher in the northern parts and lower in the center of the Northeast China and PNBN increased from southern to northern parts during the 1990–2015 period.The NUE indicators decreased with time in most counties during the study period.The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFPN,and 0.67 and 0.89 for PNBN,respectively.The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model.The planting area index of vegetables and beans,soil clay content,saturated water content,enhanced vegetation index in November&December,soil bulk density,and annual minimum temperature were the main explanatory variables for both NUE indicators.This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR.This novel study gives reference measurements to improve crop NUE which is one of the most effective means of managing N for sustainable development,ensuring food security,alleviating environmental degradation and increasing farmer’s profitability.展开更多
There are various analytical, empirical and numerical methods to calculate groundwater inflow into tun- nels excavated in rocky media. Analytical methods have been widely applied in prediction of groundwa- ter inflow ...There are various analytical, empirical and numerical methods to calculate groundwater inflow into tun- nels excavated in rocky media. Analytical methods have been widely applied in prediction of groundwa- ter inflow to tunnels due to their simplicity and practical base theory. Investigations show that the real amount of water infiltrating into jointed tunnels is much less than calculated amount using analytical methods and obtained results are very dependent on tunnel＇s geometry and environmental situations. In this study, using multiple regression analysis, a new empirical model for estimation of groundwater seepage into circular tunnels was introduced. Our data was acquired from field surveys and laboratory analysis of core samples. New regression variables were defined after perusing single and two variables relationship between groundwater seepage and other variables. Finally, an appropriate model for estima- tion of leakage was obtained using the stepwise algorithm. Statistics like R, R2, R2e and the histogram of residual values in the model represent a good reputation and fitness for this model to estimate the groundwater seepage into tunnels. The new experimental model was used for the test data and results were satisfactory. Therefore, multiple regression analysis is an effective and efficient way to estimate the groundwater seeoage into tunnels.展开更多
Background,aim,and scope Stable isotope in water could respond sensitively to the variation of environment and be reserved in different geological archives,although they are scarce in the environment.And the methods d...Background,aim,and scope Stable isotope in water could respond sensitively to the variation of environment and be reserved in different geological archives,although they are scarce in the environment.And the methods derived from the stable isotope composition of water have been widely applied in researches on hydrometeorology,weather diagnosis,and paleoclimate reconstruction,which help well for understanding the water-cycle processes in one region.Here,it is aimed to explore the temporal changes of stable isotopes in precipitation from Adelaide,Australia and determine the influencing factors at different timescales.Materials and methods Based on the isotopic data of daily precipitation over four years collected in Adelaide,Australia,the variation characteristics of dailyδD,δ^(18)O,and dexcess in precipitation and its relationship with meteorological elements were analyzed.Results The results demonstrated the local meteoric water line(LMWL)in Adelaide,wasδD=6.38×δ^(18)O+6.68,with a gradient less than 8.There is a significant negative correlation between dailyδ^(18)O and precipitation amount or relative humidity at daily timescales in both the whole year and wither/summerhalf year(p<0.001),but a significant positive correlation between dailyδ^(18)O and temperature in the whole year and the winter half-year(p<0.001).Discussion The correlation coefficients betweenδ^(18)O and daily mean temperature didn’t show a significant positive correlation,which may be attributed to that the precipitation in Adelaide area in January was mainly influenced by strong convective weather,and the stable isotope values in precipitation were significantly negative.Furthermore,this propose was also evidenced by the results from dexcess of precipitation with larger value in the winter half-year than that in the summer half-year,which may be resulted from the precipitation events in winter are mostly influenced by oceanic water vapor,while the sources of water vapor in summer precipitation events are more complicated and influenced by strong convective weather.On the other hand,the slope and intercept of theδ^(18)O—P regression lines in the summer months(-0.41 and 0.50‰)are larger and smaller than those in the winter months(-0.22 and-2.15‰),respectively,indicating that the precipitation stable isotopes have a relatively stronger rainout effect in the summer months than in the winter months.Besides,the measured values ofδ^(18)O in daily precipitation have a good linear relationship with our simulated values ofδ^(18)O,demonstrating the established regression model could provide a reliable simulation for theδ^(18)O values in daily precipitation in Adelaide area.It’s worth noting that the precipitation events with low precipitation amount,low relative humidity and high temperature,usually had relatively small slope and intercept of MWL,implying that raindrops may be strongly affected by sub-cloud secondary evaporation in the falling process.Conclusions The variation ofδ^(18)O in daily precipitation from Adelaide region was controlled by different factors at different timescales.And the water vapor sources and the meteorological conditions of precipitation events(such as the degree of sub-cloud secondary evaporation)also played an important role on the variation ofδ^(18)O.Recommendations and perspectives Stable isotope in daily precipitation can provide more accurate information about water-cycle and atmosphere circulation,it is therefore necessary to continue to collect and analyze daily-scale precipitation data over a longer time span.The results of this study will provide the basis for the fields of hydrometeorology,meteorological diagnosis and paleoclimate reconstruction in Adelaide,Australia.展开更多
Due to the complex conditions and strong heterogeneity of tight sandstone reservoirs,the reservoirs should be classified and the controlling factors of physical properties should be studied.Cast thin section observati...Due to the complex conditions and strong heterogeneity of tight sandstone reservoirs,the reservoirs should be classified and the controlling factors of physical properties should be studied.Cast thin section observations,cathodoluminescence,scanning electron microscopy(SEM),X-ray diffraction(XRD),and high-pressure mercury injection(HPMI)were used to classify and optimize the reservoir.The Brooks-Corey model and stepwise regression were used to study the fractal dimension and main controlling factors of the physical properties of the high-quality reservoir.The results show that the reservoirs in the study area can be divided into four types,and the high-quality reservoir has the best physical properties and pore-throat characteristics.In the high-quality reservoir,the homogeneity of transitional pores was the best,followed by that of micropores,and the worst was mesopores.The porosity was controlled by depth and kaolinite.The model with standardized coefficients is y=12.454−0.778×(Depth)+0.395×(Kaolinite).The permeability was controlled by depth,illite/montmorillonite,and siliceous cement,and the model with standardized coefficients is y=1.689−0.683×(Depth)−0.395×(Illite/Montmorillonite)−0.337×(Siliceous Cement).The pore-throat evolutionary model shows that the early-middle diagenetic period was when the reservoir physical properties were at their best,and the kaolinite intercrystalline pores and residual intergranular pores were the most important.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
基金The National Natural Science Foundation of China(No.50378016).
文摘Because of the difficulty to obtain the traffic flow information of lanes at non-detector intersections in most metropolises of the world,based on the relationships between the lanes of signal-controlled intersections,cluster analysis and stepwise regression are integrated to predict the traffic volume of lanes at non-detector isolated controlled intersections.First cluster analysis is used to cluster the lanes of non-detector isolated signal-controlled intersections and the lanes of all signal-controlled intersections with detectors.Then, by the results of cluster analysis,the traffic volume samples are selected randomly and stepwise regression is used to predict the traffic volume of lanes at non-detector isolated signal-controlled intersections.The method is tested by the traffic volume data of lanes of the road network of Nanjing city.The problem of predicting the traffic volume of lanes at non-detector isolated signal-controlled intersections was resolved and can be widely used in urban traffic flow guidance and urban traffic control in cities without enough intersections equipped with detectors.
基金Supported by National Key Technology R&D Program in the11th Five Year Plan of China(2006BAD10A14)~~
文摘[Objective] The research aimed to study the significant influence factors of the population variations of oriental fruit fly. [Method] Using stepwise regression analysis, the population variations law of oriental fruit fly in Jianshui County of Yunnan province and the meteorological factors that caused its occurrence were analyzed. And the regression model was built. Finally, the regression model was tested on the basis of the data in Jianshui County of Yunnan Province during 2004-2006.[Result] The main meteorological factors that influenced the occurrence of oriental fruit fly were relative humidity, the lowest monthly temperature and rainfall. [Conclusion] This study will provide certain reference for the prediction researches on the time, quantity and occurrence peak of oriental fruit fly.
文摘A statistical analysis was conducted on the feeding behavior of 106 York breeding pigs.Pearson correlation analysis,principal component correlation analysis and multiple stepwise regression equation methods were applied to establish regression equations of the York breeding pigs total feed intake per time and average feed intake per time with corrected fat thickness,feed conversion rate,and corrected daily gain.The results showed that:①there were three peak feed intake periods for the pigs,and the correlation coefficient between the feed intake and the corrected fat thickness of the pigs in the 24 h period was positive or negative,that is,increasing the number of feeding times and the feed intake was not necessarily conducive to the fat thickness accumulation,but the breeding goal of fat thickness could be achieved by controlling the feeding times and feed intake;②the average feed intake of pigs in the 60-90 kg body weight stage was 30%-50%higher than that of the 30-60 kg body weight stage,but the number of feeding times decreased,the peak feeding time was more concentrated,and the feeding duration per time was 3.0 min longer,indicating that as the weight of pigs increased,the feed intake increased significantly;and③the stepwise regression equations and the principal component equations showed that the feeding behavior of York pigs in the 30-90 kg growth stage was not only affected by the feeding time within 24 h,but also by environmental factors such as temperature and humidity.The feeding behavior of York pigs is a complex process of interaction between environmental factors and animal factors.
文摘In this paper, an overview of an important feature in statistics field has shown： the stepwise multiple linear regression. Likewise, a link between stepwise multiple linear regression and earthquakes localization has been descripted. Precisely, the aim of this research is showing how stepwise multiple linear regression contributes to solution of earthquakes localization, describing its conditions of use in HYPO71PC, a software devoted to computation of seismic sources’ collocation. This aim is reached treating a concrete case, that is computation of earthquakes localization happening on Mount Vesuvius, Italy.
基金Supported by Guizhou Agricultural Research Project(QKH[2019]2279)Construction of Guizhou Breeding Livestock and Poultry Genetic Resources Testing Platform(QKZYD[2018]4015)Scientific and Technological Innovation Talent Team of Major Livestock and Poultry Genome Big Data Analysis and Application Research in Guizhou Province(QKHPTRC[2019]5615)。
文摘The total output value of mutton in Northwestern China has accounted for more than 60%of the total output value of animal husbandry over the years.It can be seen that the mutton industry in Northwest China not only plays a pivotal role in animal husbandry,but also plays an important role in Chinese agriculture.In this study,based on cost accounting theory,income-related theories and total factor productivity theory,using basic knowledge of statistics and economics,drawing on existing research results at home and abroad,and adopting a combination of qualitative analysis and quantitative analysis of SAS multiple stepwise regression,the changing trends of cost-benefit of mutton sheep breeding in Northwest agricultural and pastoral areas and influencing factors of production costs and production efficiency were investigated,aiming to provide reference for saving mutton sheep feeding material resources,reducing mutton sheep breeding costs,and improving mutton sheep breeding benefits.
文摘This paper has compared variable selection method for multiple linear regression models that have both relative and non-relative variables in full model when predictor variables are highly correlated 0.999 . In this study two objective functions used in the Tabu Search are mean square error （MSE） and the mean absolute error （MAE）. The results of Tabu Search are compared with the results obtained by stepwise regression method based on the hit percentage criterion. The simulations cover the both cases, without and with multicollinearity problems. For each situation, 1,000 iterations are examined by applying a different sample size n = 25 and 100 at 0.05 level of significance. Without multicollinearity problem, the hit percentages of the stepwise regression method and Tabu Search using the objective function of MSE are almost the same but slightly higher than the Tabu Search using the objective function of MAE. However with multicollinearity problem the hit percentages of the Tabu Search using both objective functions are higher than the hit percentage of the stepwise regression method.
基金funded by the INTER program and cofunded by the Fond National de la Recherche,Luxembourg(FNR)and the Fund for Scientific Research-FNRS,Belgium(F.R.S-FNRS),T.0233.20-‘Sustainable Residential Densification’project(SusDens,2020–2024).
文摘The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for modelling urban growth to closely reflects reality.Despite extensive research,ambiguity remains about how variations in these input variables influence urban densification.In this study,we conduct a global sensitivity analysis(SA)using a multinomial logistic regression(MNL)model to assess the model’s explanatory and predictive power.We examine the influence of global variables,including spatial resolution,neighborhood size,and density classes,under different input combinations at a provincial scale to understand their impact on densification.Additionally,we perform a stepwise regression to identify the significant explanatory variables that are important for understanding densification in the Brussels Metropolitan Area(BMA).Our results indicate that a finer spatial resolution of 50 m and 100 m,smaller neighborhood size of 5×5 and 3×3,and specific density classes—namely 3(non-built-up,low and high built-up)and 4(non-built-up,low,medium and high built-up)—optimally explain and predict urban densification.In line with the same,the stepwise regression reveals that models with a coarser resolution of 300 m lack significant variables,reflecting a lower explanatory power for densification.This approach aids in identifying optimal and significant global variables with higher explanatory power for understanding and predicting urban densification.Furthermore,these findings are reproducible in a global urban context,offering valuable insights for planners,modelers and geographers in managing future urban growth and minimizing modelling.
基金supported in part by the National Natural Science Foundation of China(No.52177085)Science and Technology Planning Project of Guangzhou(No.202102021208)。
文摘Accurate information for consumer phase connectivity in a low-voltage distribution network(LVDN)is critical for the management of line losses and the quality of customer service.The wide application of smart meters provides the data basis for the phase identification of LVDN.However,the measurement errors,poor communication,and data distortion have significant impacts on the accuracy of phase identification.In order to solve this problem,this paper proposes a phase identification method of LVDN based on stepwise regression(SR)method.First,a multiple linear regression model based on the principle of energy conservation is established for phase identification of LVDN.Second,the SR algorithm is used to identify the consumer phase connectivity.Third,by defining a significance correction factor,the results from the SR algorithm are updated to improve the accuracy of phase identification.Finally,an LVDN test system with 63 consumers is constructed based on the real load.The simulation results prove that the identification accuracy achieved by the proposed method is higher than other phase identification methods under the influence of various errors.
基金Supported by the National Natural Science Foundation of China under Grant Nos.40675023 and 41065002the Key Natural Science Foundation of Guangxi Province under Grant No.0832019Z
文摘The prediction accuracy of the traditional stepwise regression prediction equation（SRPE）is affected by the multicollinearity among its predictors.This paper introduces the condition number analysis into the prediction modeling to minimize the multicollinearity in the SRPE.In the condition number prediction modeling,the condition number is used to select the combination of predictors with the lowest multicollinearity from the possible combinations of a number of candidate predictors（variables）,and the selected combination is then used to construct the condition number regression prediction equation（CNRPE）.This novel prediction modeling is performed in typhoon track prediction,which is a difficult task among meteorological disaster predictions.Six pairs of typhoon track latitude/longitude SRPEs and CNRPEs for July,August,and September are built by employing the traditional and the novel prediction modeling approaches,respectively,and by using a large number of identical modeling samples.The comparative analysis indicates that under the condition of the same candidate predictors（variables）and predictands（dependent variables）,although the fitting accuracy of the novel prediction models used for the historical samples of South China Sea（SCS）typhoon tracks is slightly lower than that of the traditional prediction models,the prediction accuracy for the independent samples is obviously improved,with the averaged prediction error of the novel models for July,August,and September being 153.9 kin,which is 75.3 km smaller than that of the traditional models（a reduction of 33%）.This is because the novel prediction modeling effectively minimizes the multicollinearity by computation and analysis of the condition number.It is shown further that when F=1.0,2.0,and 3.0,the average prediction errors of the traditional SRPEs are obviously larger than those of the CNRPEs.Moreover,extremely large and unreasonable prediction errors occur at some individual points of the typhoon track predicted by the SRPEs due to the multicollinearity existing in the combination of predictors.
文摘The energy sector in Poland is the source of 81% of greenhouse gas（GHG） emissions. Poland,among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO2 emissions demonstrate a high relationship（0.97） with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH4 emission, indicated the presence of hard coal（0.66）, peat and fuel wood（0.34）, solid waste fuels, as well as other sources（- 0.64） as the most important variables. The adjusted coefficient is suitable and equals R2= 0.90. For N2 O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N2 O emission is the peat and wood fuel consumption.
文摘Abstract Using the method of stepwise multivariate linear regression (SMLR), the quantitative structure activity relationships (QSAR) of two isomeric series of taxol and its derivatives have been studied. It was found that the molar refractivity of the C3′substituent of the C13 side chain has significant correlation with its activity. We deduce that structural changes in the C3′substituents may be critical to the anticancer function. It would be useful to the design and synthesis of taxol like compounds with improved activities.
文摘Aim New statistical method was applied in data analysis of orthogonal experiments to optimize the preparation of liposome. Method Particle size, zeta potential, encapsulation efficiency and physical stability of liposomes were selected by orthogonal design as evaluating indicators. Through three statistical methods （direct observation, variance analysis and stepwise multiple regression）, the optimized preparing conditions were acquired and validated by experiment. Results All of the four indicators were different by these analyses. The validation experiments indicated that the optimized conditions by stepwise multiple regressions were better than that by traditional analysis. Conclusion Experiment results suggested that multiple regressions could avoid the weakness of direct observation and variance analysis, but more work should be done in preparing liposomes.
基金supported by the Research Support Funds Grant (RSFG) program of Indiana University-Purdue University at Indianapolis
文摘Partial least squares （PLS） regression was applied to the Lunar Soft Characterization Consortium （LSCC） dataset for spectral estimation of TiO2. The LSCC dataset was split into a number of subsets including the low-Ti, high-Ti, total mare soils, total highland, Apollo 16, and Apollo 14 soils to investigate the effects of interfering minerals and nonlinearity on the PLS performance. The PLS weight loading vectors were analyzed through stepwise multiple regression analysis （SMRA） to identify mineral species driving and interfering the PLS performance. PLS exhibits high performance for estimating TiO2 for the LSCC low-Ti and high-Ti mare samples and both groups analyzed together. The results suggest that while the dominant TiO2-bearing minerals are few, additional PLS factors are required to compensate the effects on the important PLS factors of minerals that are not highly corrected to TiO2, to accommodate nonlinear relationships between reflectance and TiO2, and to correct inconsistent mineral-TiO2 correlations between the high-Ti and iow-Ti mare samples. Analysis of the LSCC highland soil samples indicates that the Apollo 16 soils are responsible for the large errors of TiO2 estimates when the soils are modeled with other subgroups. For the LSCC Apollo 16 samples, the dominant spectral effects of plagioclase over other dark minerals are primarily responsible for large errors of estimated TiO2. For the Apollo 14 soils, more accurate estimation for TiO2 is attributed to the posi- tive correlation between a major TiOe-bearing component and TiO2, explaining why the Apollo 14 soils follow the regression trend when analyzed with other soils groups.
基金supported by the Major Program of the National Natural Science Foundation of China(No.42090054)the National Key Scientific Instrument and Equipment Development Projects of China(No.41827808)+1 种基金the Major Program of the National Natural Science Foundation of China(No.42090055)the National Science Foundation of China(No.42107194)。
文摘In the physical model test of landslides,the selection of analogous materials is the key,and it is difficult to consider the similarity of mechanical properties and seepage performance at the same time.To develop a model material suitable for analysing the deformation and failure of reservoir landslides,based on the existing research foundation of analogous materials,5 materials and 5 physical-mechanical parameters were selected to design an orthogonal test.The factor sensitivity of each component ratio and its influence on the physical-mechanical indices were studied by range analysis and stepwise regression analysis,and the proportioning method was determined.Finally,the model material was developed,and a model test was carried out considering Huangtupo as the prototype application.The results showed that(1)the model material composed of sand,barite powder,glass beads,clay,and bentonite had a wide distribution of physical-mechanical parameters,which could be applied to model tests under different conditions;(2)the physical-mechanical parameters of analogous materials matched the application prototype;and(3)the mechanical properties and seepage performance of the model material sample met the requirements of reservoir landslide model tests,which could be used to simulate landslide evolution and analyse the deformation process.
基金supported by the National Natural Science Foundation of China(Nos.U2340226,12072047,42277186)China Three Gorges Corporation under the contract of No.0799291(SXSN/5115)the Fundamental Research Funds for Central Public Welfare Research Institutes(CKSF20241014/YT,CKSF20241016/YT).
文摘The Yemaomian landslide,the largest near-dam accumulation landslide in the Three Gorges Reservoir area,is situated 17 km upstream of the Three Gorges Dam.Nearly 20 years of monitoring data indicate that the landslide has been undergoing slow deformation with a low deformation rate and magnitude.This paper applies a stepwise linear regression method and a mechanical model of hydrodynamics triggering to deeply explore the relationship between geological conditions,external factors,and deformation characteristics.Based on the stage transition characteristics of external triggering factors,the deformation evolution process of the landslide since the reservoir impoundment is divided into three stages:(1)June 2003-September 2006,the landslide was reactivated by the significant rise in reservoir water levels,in a retrogressive mode;(2)October 2006-September 2018,the deformation mode shifted from retrogressive mode to creep deformation as a whole,primarily due to the degradation effect on the landslide mass caused by immersion in reservoir water.(3)October 2018-February 2024,a further significant reduction in the overall deformation rate and the impact of seasonal rainfall on landslide deformation surpassed that of reservoir water level fluctuations.The main component of landslide deformation is convergent creep,and extreme rainfall will be an important triggering factor for the local instability.Identifying the deformation evolution stages and determining the dominant external influencing factors at each stage is crucial for landslide research,and this paper provides an effective research paradigm for this purpose.
基金the China Scholarship Council(CSC)(201903250115)the National Natural Science Foundation of China(31972515)the China Agriculture Research System of MOF and MARA(CARS-09-P31).
文摘Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-researched.In this study,stepwise multiple linear regression(SMLR)and Random Forest(RF)were used to evaluate the spatial and temporal variation of NUE indicators(i.e.,partial factor productivity of N(PFPN);partial nutrient balance of N(PNBN))at county scale in Northeast China(Heilongjiang,Liaoning and Jilin provinces)from 1990 to 2015.Explanatory variables included agricultural management practices,topography,climate,economy,soil and crop types.Results revealed that the PFPN was higher in the northern parts and lower in the center of the Northeast China and PNBN increased from southern to northern parts during the 1990–2015 period.The NUE indicators decreased with time in most counties during the study period.The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFPN,and 0.67 and 0.89 for PNBN,respectively.The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model.The planting area index of vegetables and beans,soil clay content,saturated water content,enhanced vegetation index in November&December,soil bulk density,and annual minimum temperature were the main explanatory variables for both NUE indicators.This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR.This novel study gives reference measurements to improve crop NUE which is one of the most effective means of managing N for sustainable development,ensuring food security,alleviating environmental degradation and increasing farmer’s profitability.
文摘There are various analytical, empirical and numerical methods to calculate groundwater inflow into tun- nels excavated in rocky media. Analytical methods have been widely applied in prediction of groundwa- ter inflow to tunnels due to their simplicity and practical base theory. Investigations show that the real amount of water infiltrating into jointed tunnels is much less than calculated amount using analytical methods and obtained results are very dependent on tunnel＇s geometry and environmental situations. In this study, using multiple regression analysis, a new empirical model for estimation of groundwater seepage into circular tunnels was introduced. Our data was acquired from field surveys and laboratory analysis of core samples. New regression variables were defined after perusing single and two variables relationship between groundwater seepage and other variables. Finally, an appropriate model for estima- tion of leakage was obtained using the stepwise algorithm. Statistics like R, R2, R2e and the histogram of residual values in the model represent a good reputation and fitness for this model to estimate the groundwater seepage into tunnels. The new experimental model was used for the test data and results were satisfactory. Therefore, multiple regression analysis is an effective and efficient way to estimate the groundwater seeoage into tunnels.
文摘Background,aim,and scope Stable isotope in water could respond sensitively to the variation of environment and be reserved in different geological archives,although they are scarce in the environment.And the methods derived from the stable isotope composition of water have been widely applied in researches on hydrometeorology,weather diagnosis,and paleoclimate reconstruction,which help well for understanding the water-cycle processes in one region.Here,it is aimed to explore the temporal changes of stable isotopes in precipitation from Adelaide,Australia and determine the influencing factors at different timescales.Materials and methods Based on the isotopic data of daily precipitation over four years collected in Adelaide,Australia,the variation characteristics of dailyδD,δ^(18)O,and dexcess in precipitation and its relationship with meteorological elements were analyzed.Results The results demonstrated the local meteoric water line(LMWL)in Adelaide,wasδD=6.38×δ^(18)O+6.68,with a gradient less than 8.There is a significant negative correlation between dailyδ^(18)O and precipitation amount or relative humidity at daily timescales in both the whole year and wither/summerhalf year(p<0.001),but a significant positive correlation between dailyδ^(18)O and temperature in the whole year and the winter half-year(p<0.001).Discussion The correlation coefficients betweenδ^(18)O and daily mean temperature didn’t show a significant positive correlation,which may be attributed to that the precipitation in Adelaide area in January was mainly influenced by strong convective weather,and the stable isotope values in precipitation were significantly negative.Furthermore,this propose was also evidenced by the results from dexcess of precipitation with larger value in the winter half-year than that in the summer half-year,which may be resulted from the precipitation events in winter are mostly influenced by oceanic water vapor,while the sources of water vapor in summer precipitation events are more complicated and influenced by strong convective weather.On the other hand,the slope and intercept of theδ^(18)O—P regression lines in the summer months(-0.41 and 0.50‰)are larger and smaller than those in the winter months(-0.22 and-2.15‰),respectively,indicating that the precipitation stable isotopes have a relatively stronger rainout effect in the summer months than in the winter months.Besides,the measured values ofδ^(18)O in daily precipitation have a good linear relationship with our simulated values ofδ^(18)O,demonstrating the established regression model could provide a reliable simulation for theδ^(18)O values in daily precipitation in Adelaide area.It’s worth noting that the precipitation events with low precipitation amount,low relative humidity and high temperature,usually had relatively small slope and intercept of MWL,implying that raindrops may be strongly affected by sub-cloud secondary evaporation in the falling process.Conclusions The variation ofδ^(18)O in daily precipitation from Adelaide region was controlled by different factors at different timescales.And the water vapor sources and the meteorological conditions of precipitation events(such as the degree of sub-cloud secondary evaporation)also played an important role on the variation ofδ^(18)O.Recommendations and perspectives Stable isotope in daily precipitation can provide more accurate information about water-cycle and atmosphere circulation,it is therefore necessary to continue to collect and analyze daily-scale precipitation data over a longer time span.The results of this study will provide the basis for the fields of hydrometeorology,meteorological diagnosis and paleoclimate reconstruction in Adelaide,Australia.
基金financially supported by the National Natural Science Foundation of China(Nos.41972172 and U1910205).
文摘Due to the complex conditions and strong heterogeneity of tight sandstone reservoirs,the reservoirs should be classified and the controlling factors of physical properties should be studied.Cast thin section observations,cathodoluminescence,scanning electron microscopy(SEM),X-ray diffraction(XRD),and high-pressure mercury injection(HPMI)were used to classify and optimize the reservoir.The Brooks-Corey model and stepwise regression were used to study the fractal dimension and main controlling factors of the physical properties of the high-quality reservoir.The results show that the reservoirs in the study area can be divided into four types,and the high-quality reservoir has the best physical properties and pore-throat characteristics.In the high-quality reservoir,the homogeneity of transitional pores was the best,followed by that of micropores,and the worst was mesopores.The porosity was controlled by depth and kaolinite.The model with standardized coefficients is y=12.454−0.778×(Depth)+0.395×(Kaolinite).The permeability was controlled by depth,illite/montmorillonite,and siliceous cement,and the model with standardized coefficients is y=1.689−0.683×(Depth)−0.395×(Illite/Montmorillonite)−0.337×(Siliceous Cement).The pore-throat evolutionary model shows that the early-middle diagenetic period was when the reservoir physical properties were at their best,and the kaolinite intercrystalline pores and residual intergranular pores were the most important.
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.
