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
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
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
Soybean frogeye leaf spot(FLS) disease is a global disease affecting soybean yield, especially in the soybean growing area of Heilongjiang Province. In order to realize genomic selection breeding for FLS resistance of...Soybean frogeye leaf spot(FLS) disease is a global disease affecting soybean yield, especially in the soybean growing area of Heilongjiang Province. In order to realize genomic selection breeding for FLS resistance of soybean, least absolute shrinkage and selection operator(LASSO) regression and stepwise regression were combined, and a genomic selection model was established for 40 002 SNP markers covering soybean genome and relative lesion area of soybean FLS. As a result, 68 molecular markers controlling soybean FLS were detected accurately, and the phenotypic contribution rate of these markers reached 82.45%. In this study, a model was established, which could be used directly to evaluate the resistance of soybean FLS and to select excellent offspring. This research method could also provide ideas and methods for other plants to breeding in disease resistance.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
Soil texture is an indicator of soil physical structure which delivers many ecological functions of soils such as thermal regime, plant growth, and soil quality. However, traditional methods for soil texture measureme...Soil texture is an indicator of soil physical structure which delivers many ecological functions of soils such as thermal regime, plant growth, and soil quality. However, traditional methods for soil texture measurement are time-consuming and labor-intensive. This study attempts to explore an indirect method for rapid estimating the texture of three subgroups of purple soils (i.e. calcareous, neutral, and acidic). 190 topsoil (0 - 10 cm) samples were collected from sloping croplands in Tongnan and Beibei Districts of Chongqing Municipality in China. Vis-NIR spectrum was measured and processed, and stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), and back propagation neural network (BPNN) models were constructed to inform the soil texture. The clay fractions ranged from 4.40% to 27.12% while sand fractions ranged from 0.34% to 36.57%, hereby soil samples encompass three textural classes (i.e. silt, silt loam, and silty clay loam). For the original spectrum, the texture of calcareous and neutral purple soils was not significantly correlated with spectral reflectance and linear models (SMLR and PLSR) exhibited low prediction accuracy. The correlation coefficients and the goodness-of-fits between soil texture and the transformed spectra of all soil groups increased by continuum-removal (CR), first-order differential (R'), and second-order differential (R") transformations. Among them, the R" had the best performance in terms of improving the correlation coefficients and the goodness-of-fits. For the calcareous purple soil, the SMLR exceeds PLSR and BPNN with a higher coefficient of determination (R<sup>2</sup>) and the ratio of performance to inter-quartile distance (RPIQ) values and lower root mean square error of validation (RMSEV), but for the neutral and acidic purple soils, the PLSR model has a better prediction accuracy. In summary, the linear methods (SMLR and PLSR) are more reliable in estimating the texture of the three purple soil groups when using Vis-NIR spectroscopy inversion.展开更多
The COVID-19 pandemic has resulted in over 33 million confirmed cases and over 1 million deaths globally,as of 1 October 2020.During the lockdown and restrictions placed on public activities and gatherings,green space...The COVID-19 pandemic has resulted in over 33 million confirmed cases and over 1 million deaths globally,as of 1 October 2020.During the lockdown and restrictions placed on public activities and gatherings,green spaces have become one of the only sources of resilience amidst the coronavirus pandemic,in part because of their positive effects on psychological,physical and social cohesion and spiritual wellness.This study analyzes the impacts of COVID-19 and government response policies to the pandemic on park visitation at global,regional and national levels and assesses the importance of parks during this global pandemic.The data we collected primarily from Google’s Community Mobility Reports and the Oxford Coronavirus Government Response Tracker.The results for most countries included in the analysis show that park visitation has increased since February 16th,2020 compared to visitor numbers prior to the COVID-19 pandemic.Restrictions on social gathering,movement,and the closure of workplace and indoor recreational places,are correlated with more visits to parks.Stay-at-home restrictions and government stringency index are negatively associated with park visits at a global scale.Demand from residents for parks and outdoor green spaces has increased since the outbreak began,and highlights the important role and benefits provided by parks,especially urban and community parks,under the COVID-19 pandemic.We provide recommendations for park managers and other decision-makers in terms of park management and planning during health crises,as well as for park design and development.In particular,parks could be utilized during pandemics to increase the physical and mental health and social well-being of individuals.展开更多
Visible and near-infrared(VNIR)spectroscopy is an eco-friendly method used for estimating plant nutrient deficiencies.The aim of this study was to investigate the possibility of using VNIR method for estimating Zn con...Visible and near-infrared(VNIR)spectroscopy is an eco-friendly method used for estimating plant nutrient deficiencies.The aim of this study was to investigate the possibility of using VNIR method for estimating Zn content in cherry orchard leaves under field conditions.The study was conducted in 3different locations in Isparta region of Turkey.Fifteen cherry orchards containing normal and Zn deficient plants were chosen,and 60 leaf samples were collected from each location.The reflectance spectra of the leaves were measured with an ASD FieldSpec HandHeld spectroradiometer and a plant probe.The Zn contents of leaf samples were predicted through laboratory analysis.The spectral reflectance measurements were used to estimate the Zn levels using stepwise multiple linear regression analysis method.Prediction models were created using the highest coefficient of determination value.The results show that Zn content of cherry trees can be estimated using the VNIR spectroscopic method(87.5<r2<96.79).Moreover,plant nutrient contents can be estimated without using chemicals.However,further research is necessary to develop a standard method for field conditions.Because spectral reflectance is affected by ecological conditions,agricultural applications and nutrient interactions,more effective models must be developed depending on the geographical location,period and plant type.展开更多
The deformation prediction models of Wuqiangxi concrete gravity dam are developed,including two statistical models and a deep learning model.In the statistical models,the reliable monitoring data are firstly determine...The deformation prediction models of Wuqiangxi concrete gravity dam are developed,including two statistical models and a deep learning model.In the statistical models,the reliable monitoring data are firstly determined with Lahitte criterion;then,the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data,and the factors of water pressure,temperature and time effect are considered in the models;finally,according to the monitoring data from 2006 to 2020 of five typical measuring points including J23(on dam section 24^(#)),J33(on dam section 4^(#)),J35(on dam section 8^(#)),J37(on dam section 12^(#)),and J39(on dam section 15^(#))located on the crest of Wuqiangxi concrete gravity dam,the settlement curves of the measuring points are obtained with the stepwise regression and partial least squares regression models.A deep learning model is developed based on long short-term memory(LSTM)recurrent neural network.In the LSTM model,two LSTMlayers are used,the rectified linear unit function is adopted as the activation function,the input sequence length is 20,and the random search is adopted.The monitoring data for the five typical measuring points from 2006 to 2017 are selected as the training set,and the monitoring data from 2018 to 2020 are taken as the test set.From the results of case study,we can find that(1)the good fitting results can be obtained with the two statistical models;(2)the partial least squares regression algorithm can solve the model with high correlation factors and reasonably explain the factors;(3)the prediction accuracy of the LSTM model increases with increasing the amount of training data.In the deformation prediction of concrete gravity dam,the LSTM model is suggested when there are sufficient training data,while the partial least squares regression method is suggested when the training data are insufficient.展开更多
Researches of glaucoma visual function damage, hemorrheololgy, ocular rheography and other related multiplex factors, with computed multifactorial stepwise regresion analysis, indicate that the elevation of intraocula...Researches of glaucoma visual function damage, hemorrheololgy, ocular rheography and other related multiplex factors, with computed multifactorial stepwise regresion analysis, indicate that the elevation of intraocular pressure (IOP) is not the only factor to induce visual impairment. POAG patients are shown to have markedly reduced diastolic purfussion pressure in ophthalmic artery, besides prolonged filling time of the retinal artery and vein, diminished erythrocyte deformability and increased platele...展开更多
From the perspective of the tactile comfort of underwear fabrics, 179 kinds of underwear fabrics were selected to test tactile related performance indices using the fabric touch tester(FTT), and the relationship betwe...From the perspective of the tactile comfort of underwear fabrics, 179 kinds of underwear fabrics were selected to test tactile related performance indices using the fabric touch tester(FTT), and the relationship between physical indicators and tactile sensation of different fiber types of underwear fabrics was studied to establish a digital regression model by a stepwise regression method. The experimental results show that fabric fiber composition, compression characteristics, surface friction coefficient, surface roughness amplitude, bending characteristics, and maximum thermal conductivity significantly affect the level of tactile comfort of underwear fabrics, the composition of underwear fabrics has a significant effect on soft touch, and the clustering method and the grading method can effectively rate the level of tactile comfort of underwear fabrics.展开更多
Fatigue has negative impacts on the general working population as well as on seafarers. In order to study seafarers’ fatigue, a questionnaire-base survey was conducted to gain information about potential risk factors...Fatigue has negative impacts on the general working population as well as on seafarers. In order to study seafarers’ fatigue, a questionnaire-base survey was conducted to gain information about potential risk factors for fatigue and construct indexes indicating fatigue. The study applies T-test to compare strata of seafarers to analyse work and sleep patterns in global seafaring. Qualitative analysis are also employed to explore the impacts of fatigue on seafarer’s occupational health and safety.展开更多
In this work, 10 batches of Salvia miltiorrhiza concentrate were prepared and purified with ethanol precipitation process. Dry matter content, pH value, conductivity and water content of the concentrates and supernata...In this work, 10 batches of Salvia miltiorrhiza concentrate were prepared and purified with ethanol precipitation process. Dry matter content, pH value, conductivity and water content of the concentrates and supernatants were all determined. When more ethanol was used in ethanol precipitation, the pH value of the supernatant generally increased, but dry matter content, water content, and the conductivity decreased. Multivariate linear models were built with the most determination coefficient values greater than 0.7. More than 80% of stachyose was removed in the ethanol precipitation process. The removal rate of fructose, raffinose and sucrose were all higher than 30%. When ethanol addition amount increased, the purity of phenolic acids in the supernatant increased, but the retention of lithosperimic acid and salvianolic acid B decreased. The conductivity and pH value of concentrated extract show relatively small influences on ethanol precipitation indices. When fructose, raffinose, or stachyose contents in the concentrated extract were high, the retention rate of phenolic acids tends to be low on most occasions. The purity and retention rate of phenolic acids in the supernatants were also affected by the purity of phenolic acids in the concentrated. The sugar contents in the concentrate are suggested to be monitored in industry because they significantly affect ethanol precipitation process indices.展开更多
China has a vast land area and frequent interconnections between various regions.China's transportation industry is faced with tremendous pressure.This article combines China’s railway and highway transportation ...China has a vast land area and frequent interconnections between various regions.China's transportation industry is faced with tremendous pressure.This article combines China’s railway and highway transportation conditions to predict China’s economic development,uses stepwise regression to screen explanatory variables,and finally determines railway passenger turnover,road freight volume and passenger car ownership as the explanatory variables,and GDP as the dependent variable,and also analyzes China’s economic development by establish ing a multiple regression model.展开更多
We built a models to deal with the problems, including how to select the best coach, how to build a reasonable evaluation system, and how to make our model applied in any situation. The name of model is Stepwise Regre...We built a models to deal with the problems, including how to select the best coach, how to build a reasonable evaluation system, and how to make our model applied in any situation. The name of model is Stepwise Regression. We need to do the normalization processing of data and then through the step-by-step calculation only several factors were left. We find six factors to evaluate the basketball coaches, and we select two factors are important. Finally, we built an expression to calculate. By calculate the two factors data on the expression, we can rank these coaches, find the best basketball coach.展开更多
Soil moisture is essential for plant growth in terrestrial ecosystems.This study investigated the visible-near infrared(Vis-NIR)spectra of three subgroups of purple soils(calcareous,neutral,and acidic)from western Cho...Soil moisture is essential for plant growth in terrestrial ecosystems.This study investigated the visible-near infrared(Vis-NIR)spectra of three subgroups of purple soils(calcareous,neutral,and acidic)from western Chongqing,China,containing different water contents.The relationship between soil moisture and spectral reflectivity(R)was analyzed using four spectral transformations,and estimation models were established for estimating the soil moisture content(SMC)of purple soil based on stepwise multiple linear regression(SMLR)and partial least squares regression(PLSR).We found that soil spectra were similar for different moisture contents,with reflectivity decreasing with increasing moisture content and following the order neutral>calcareous>acidic purple soil(at constant moisture content).Three of the four spectral transformations can highlight spectral sensitivity to SMC and significantly improve the correlation between the reflectance spectra and SMC.SMLR and PLSRmethods provide similar prediction accuracy.The PLSR-based model using a first-order reflectivity differential(R?)is more effective for estimating the SMC,and gave coefficient of determination(v2),root mean square errors of validation(RMSEV),and ratio of performance to inter-quartile distance(RPIQ)values of 0.946,1.347,and 6.328,respectively,for the calcareous purple soil,and 0.944,1.818,and 6.569,respectively,for the acidic purple soil.For neutral purple soil,the best prediction was obtained using the SMLR method with R?transformation,yieldingv2,RMSEV and RPIQ values of 0.973,0.888 and 8.791,respectively.In general,PLSR is more suitable than SMLR for estimating the SMC of purple soil.展开更多
To explore the present status of Critical thinking and its relevant factors among undergraduates.A stratified random sampling was used to select 1013 undergraduates from 7 full-time colleges in Guangdong province.They...To explore the present status of Critical thinking and its relevant factors among undergraduates.A stratified random sampling was used to select 1013 undergraduates from 7 full-time colleges in Guangdong province.They were investigated with California Critical Thinking Disposition Inventory-Chinese Version(CTDI-CV)and a Self-Compiled Personal General Information Questionnaire.(1)The total score of CTDI-CV was(254.16±38.80).The undergraduates in the four levels of critical thinking of comprehensive strong,relatively strong,contradictory scope and serious opposition accounted for 1.78%,5.31%,87.4%and 5.51%of this group,respectively.(2)Multiple stepwise linear regression showed that the total score of CTDI-CV was positively correlated with the following 10 factors such as grade,family economic status,part-time experience,the teaching method used most commonly,like reading logic books,like reading reviews or essays,father’s warmth,mother’s warmth,openness and responsibility(β=.142 to.701,all P<.05).The following 5 factors such as father’s negation,father’s overprotection,mother’s negation,mother’s overprotection and neuroticism were negatively correlated with the total score of CTDI-CV(β=-.381 to-.616,all P<0.05).The overall level of critical thinking among undergraduates is relatively low.College Students’critical thinking may be related to many factors such as family rearing,school education and personal characteristics.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
Estimating heavy metal(HM) distribution with high precision is the key to effectively preventing Chinese medicinal plants from being polluted by the native soil. A total of 44 surface soil samples were gathered to det...Estimating heavy metal(HM) distribution with high precision is the key to effectively preventing Chinese medicinal plants from being polluted by the native soil. A total of 44 surface soil samples were gathered to detect the concentrations of eight HMs(As, Hg, Cu, Cr, Ni, Zn, Pb, and Cd) in the herb growing area of Luanping County, northeastern Hebei Province, China. An absolute principal component score-multiple linear regression(APCS-MLR) model was used to quantify pollution source contributions to soil HMs. Furthermore, the source contribution rates and environmental data of each sampling point were simultaneously incorporated into a stepwise linear regression model to identify the crucial indicators for predicting soil HM spatial distributions. Results showed that 88% of Cu, 72% of Cr, and 72% of Ni came from natural sources;50% of Zn, 49% of Pb, and 59% of Cd were mainly caused by agricultural activities;and 44% of As and 56% of Hg originated from industrial activities. When three-type(natural, agricultural, and industrial) source contribution rates and environmental data were simultaneously incorporated into the stepwise linear regression model, the fitting accuracy was significantly improved and the model could explain 31%–86% of the total variance in soil HM concentrations. This study introduced three-type source contributions of each sampling point based on APCS-MLR analysis as new covariates to improve soil HM estimation precision, thus providing a new approach for predicting the spatial distribution of HMs using small sample sizes at the county scale.展开更多
Air-Gap Diffusion Distillation(AGDD) is a new technology aiming at solving the problem of the safety of drinking water for residents in remote areas that uses a super hydrophilic porous medium as the hot channel and e...Air-Gap Diffusion Distillation(AGDD) is a new technology aiming at solving the problem of the safety of drinking water for residents in remote areas that uses a super hydrophilic porous medium as the hot channel and evaporation surface. In the experiment, it was found that the parameters of porous media have a significant influence on the desalination(evaporation) efficiency of AGDD. Although porous media are widely used as evaporation components, the factors affecting their evaporation efficiency are not fully understood. The evaporation process in super hydrophilic porous media is rarely discussed. A large number of experiments have been carried out based on AGDD. The introduction of statistical methods solves the problem that experiments cannot distinguish the contribution of complex parameters of porous media to evaporation efficiency. Stepwise regression analysis is used to reduce the dimensionality of the independent variables and construct regression equations(coefficient of determination R~2 reached 81.3%-96.8%). Evaporation flux correlations and dimensionless mass transfer correlations are established based on porous media parameters. We found that the surface evaporation of super hydrophilic porous media can be divided into three stages: diffusion evaporation, capillary evaporation, and thermal evaporation. The evaporation efficiency of these three stages is controlled by the vapor diffusion process resistance, capillary force, and energy supply. At low saturation, evaporation efficiency is limited by the resistance of the vapor diffusion process. The evaporation efficiency of the porous media is affected predominantly by the pore size, the specific surface area, porosity and the characteristic length. At high saturation, the evaporation efficiency becomes influenced primarily by the permeability. A small thickness and a high hydrophilicity also improve the evaporation efficiency.展开更多
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.
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.
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 Key Research and Development Program of China(2021YFD1201103-01-05)。
文摘Soybean frogeye leaf spot(FLS) disease is a global disease affecting soybean yield, especially in the soybean growing area of Heilongjiang Province. In order to realize genomic selection breeding for FLS resistance of soybean, least absolute shrinkage and selection operator(LASSO) regression and stepwise regression were combined, and a genomic selection model was established for 40 002 SNP markers covering soybean genome and relative lesion area of soybean FLS. As a result, 68 molecular markers controlling soybean FLS were detected accurately, and the phenotypic contribution rate of these markers reached 82.45%. In this study, a model was established, which could be used directly to evaluate the resistance of soybean FLS and to select excellent offspring. This research method could also provide ideas and methods for other plants to breeding in disease resistance.
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Soil texture is an indicator of soil physical structure which delivers many ecological functions of soils such as thermal regime, plant growth, and soil quality. However, traditional methods for soil texture measurement are time-consuming and labor-intensive. This study attempts to explore an indirect method for rapid estimating the texture of three subgroups of purple soils (i.e. calcareous, neutral, and acidic). 190 topsoil (0 - 10 cm) samples were collected from sloping croplands in Tongnan and Beibei Districts of Chongqing Municipality in China. Vis-NIR spectrum was measured and processed, and stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), and back propagation neural network (BPNN) models were constructed to inform the soil texture. The clay fractions ranged from 4.40% to 27.12% while sand fractions ranged from 0.34% to 36.57%, hereby soil samples encompass three textural classes (i.e. silt, silt loam, and silty clay loam). For the original spectrum, the texture of calcareous and neutral purple soils was not significantly correlated with spectral reflectance and linear models (SMLR and PLSR) exhibited low prediction accuracy. The correlation coefficients and the goodness-of-fits between soil texture and the transformed spectra of all soil groups increased by continuum-removal (CR), first-order differential (R'), and second-order differential (R") transformations. Among them, the R" had the best performance in terms of improving the correlation coefficients and the goodness-of-fits. For the calcareous purple soil, the SMLR exceeds PLSR and BPNN with a higher coefficient of determination (R<sup>2</sup>) and the ratio of performance to inter-quartile distance (RPIQ) values and lower root mean square error of validation (RMSEV), but for the neutral and acidic purple soils, the PLSR model has a better prediction accuracy. In summary, the linear methods (SMLR and PLSR) are more reliable in estimating the texture of the three purple soil groups when using Vis-NIR spectroscopy inversion.
基金supported by the APFNet National Park Research Project(2017SP2-UBC).
文摘The COVID-19 pandemic has resulted in over 33 million confirmed cases and over 1 million deaths globally,as of 1 October 2020.During the lockdown and restrictions placed on public activities and gatherings,green spaces have become one of the only sources of resilience amidst the coronavirus pandemic,in part because of their positive effects on psychological,physical and social cohesion and spiritual wellness.This study analyzes the impacts of COVID-19 and government response policies to the pandemic on park visitation at global,regional and national levels and assesses the importance of parks during this global pandemic.The data we collected primarily from Google’s Community Mobility Reports and the Oxford Coronavirus Government Response Tracker.The results for most countries included in the analysis show that park visitation has increased since February 16th,2020 compared to visitor numbers prior to the COVID-19 pandemic.Restrictions on social gathering,movement,and the closure of workplace and indoor recreational places,are correlated with more visits to parks.Stay-at-home restrictions and government stringency index are negatively associated with park visits at a global scale.Demand from residents for parks and outdoor green spaces has increased since the outbreak began,and highlights the important role and benefits provided by parks,especially urban and community parks,under the COVID-19 pandemic.We provide recommendations for park managers and other decision-makers in terms of park management and planning during health crises,as well as for park design and development.In particular,parks could be utilized during pandemics to increase the physical and mental health and social well-being of individuals.
文摘Visible and near-infrared(VNIR)spectroscopy is an eco-friendly method used for estimating plant nutrient deficiencies.The aim of this study was to investigate the possibility of using VNIR method for estimating Zn content in cherry orchard leaves under field conditions.The study was conducted in 3different locations in Isparta region of Turkey.Fifteen cherry orchards containing normal and Zn deficient plants were chosen,and 60 leaf samples were collected from each location.The reflectance spectra of the leaves were measured with an ASD FieldSpec HandHeld spectroradiometer and a plant probe.The Zn contents of leaf samples were predicted through laboratory analysis.The spectral reflectance measurements were used to estimate the Zn levels using stepwise multiple linear regression analysis method.Prediction models were created using the highest coefficient of determination value.The results show that Zn content of cherry trees can be estimated using the VNIR spectroscopic method(87.5<r2<96.79).Moreover,plant nutrient contents can be estimated without using chemicals.However,further research is necessary to develop a standard method for field conditions.Because spectral reflectance is affected by ecological conditions,agricultural applications and nutrient interactions,more effective models must be developed depending on the geographical location,period and plant type.
文摘The deformation prediction models of Wuqiangxi concrete gravity dam are developed,including two statistical models and a deep learning model.In the statistical models,the reliable monitoring data are firstly determined with Lahitte criterion;then,the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data,and the factors of water pressure,temperature and time effect are considered in the models;finally,according to the monitoring data from 2006 to 2020 of five typical measuring points including J23(on dam section 24^(#)),J33(on dam section 4^(#)),J35(on dam section 8^(#)),J37(on dam section 12^(#)),and J39(on dam section 15^(#))located on the crest of Wuqiangxi concrete gravity dam,the settlement curves of the measuring points are obtained with the stepwise regression and partial least squares regression models.A deep learning model is developed based on long short-term memory(LSTM)recurrent neural network.In the LSTM model,two LSTMlayers are used,the rectified linear unit function is adopted as the activation function,the input sequence length is 20,and the random search is adopted.The monitoring data for the five typical measuring points from 2006 to 2017 are selected as the training set,and the monitoring data from 2018 to 2020 are taken as the test set.From the results of case study,we can find that(1)the good fitting results can be obtained with the two statistical models;(2)the partial least squares regression algorithm can solve the model with high correlation factors and reasonably explain the factors;(3)the prediction accuracy of the LSTM model increases with increasing the amount of training data.In the deformation prediction of concrete gravity dam,the LSTM model is suggested when there are sufficient training data,while the partial least squares regression method is suggested when the training data are insufficient.
基金This Study was supported by China National Ministry of Health Young Grants(1987)Dr. Y. T. Fox Fund for Young Education of China NationaI Committee of Education(1989)
文摘Researches of glaucoma visual function damage, hemorrheololgy, ocular rheography and other related multiplex factors, with computed multifactorial stepwise regresion analysis, indicate that the elevation of intraocular pressure (IOP) is not the only factor to induce visual impairment. POAG patients are shown to have markedly reduced diastolic purfussion pressure in ophthalmic artery, besides prolonged filling time of the retinal artery and vein, diminished erythrocyte deformability and increased platele...
基金2021 Graduate Research Innovation Project of BIFT,China (No.X2021-020)。
文摘From the perspective of the tactile comfort of underwear fabrics, 179 kinds of underwear fabrics were selected to test tactile related performance indices using the fabric touch tester(FTT), and the relationship between physical indicators and tactile sensation of different fiber types of underwear fabrics was studied to establish a digital regression model by a stepwise regression method. The experimental results show that fabric fiber composition, compression characteristics, surface friction coefficient, surface roughness amplitude, bending characteristics, and maximum thermal conductivity significantly affect the level of tactile comfort of underwear fabrics, the composition of underwear fabrics has a significant effect on soft touch, and the clustering method and the grading method can effectively rate the level of tactile comfort of underwear fabrics.
文摘Fatigue has negative impacts on the general working population as well as on seafarers. In order to study seafarers’ fatigue, a questionnaire-base survey was conducted to gain information about potential risk factors for fatigue and construct indexes indicating fatigue. The study applies T-test to compare strata of seafarers to analyse work and sleep patterns in global seafaring. Qualitative analysis are also employed to explore the impacts of fatigue on seafarer’s occupational health and safety.
文摘In this work, 10 batches of Salvia miltiorrhiza concentrate were prepared and purified with ethanol precipitation process. Dry matter content, pH value, conductivity and water content of the concentrates and supernatants were all determined. When more ethanol was used in ethanol precipitation, the pH value of the supernatant generally increased, but dry matter content, water content, and the conductivity decreased. Multivariate linear models were built with the most determination coefficient values greater than 0.7. More than 80% of stachyose was removed in the ethanol precipitation process. The removal rate of fructose, raffinose and sucrose were all higher than 30%. When ethanol addition amount increased, the purity of phenolic acids in the supernatant increased, but the retention of lithosperimic acid and salvianolic acid B decreased. The conductivity and pH value of concentrated extract show relatively small influences on ethanol precipitation indices. When fructose, raffinose, or stachyose contents in the concentrated extract were high, the retention rate of phenolic acids tends to be low on most occasions. The purity and retention rate of phenolic acids in the supernatants were also affected by the purity of phenolic acids in the concentrated. The sugar contents in the concentrate are suggested to be monitored in industry because they significantly affect ethanol precipitation process indices.
文摘China has a vast land area and frequent interconnections between various regions.China's transportation industry is faced with tremendous pressure.This article combines China’s railway and highway transportation conditions to predict China’s economic development,uses stepwise regression to screen explanatory variables,and finally determines railway passenger turnover,road freight volume and passenger car ownership as the explanatory variables,and GDP as the dependent variable,and also analyzes China’s economic development by establish ing a multiple regression model.
文摘We built a models to deal with the problems, including how to select the best coach, how to build a reasonable evaluation system, and how to make our model applied in any situation. The name of model is Stepwise Regression. We need to do the normalization processing of data and then through the step-by-step calculation only several factors were left. We find six factors to evaluate the basketball coaches, and we select two factors are important. Finally, we built an expression to calculate. By calculate the two factors data on the expression, we can rank these coaches, find the best basketball coach.
基金funded by Chongqing Talent Program(CQYC201905009)Chongqing Education Commission(KJZD-K201800502,KJQN201800531)Science Fund for Distinguished Young Scholars of Chongqing(cstc2019jcyjjq X0025)。
文摘Soil moisture is essential for plant growth in terrestrial ecosystems.This study investigated the visible-near infrared(Vis-NIR)spectra of three subgroups of purple soils(calcareous,neutral,and acidic)from western Chongqing,China,containing different water contents.The relationship between soil moisture and spectral reflectivity(R)was analyzed using four spectral transformations,and estimation models were established for estimating the soil moisture content(SMC)of purple soil based on stepwise multiple linear regression(SMLR)and partial least squares regression(PLSR).We found that soil spectra were similar for different moisture contents,with reflectivity decreasing with increasing moisture content and following the order neutral>calcareous>acidic purple soil(at constant moisture content).Three of the four spectral transformations can highlight spectral sensitivity to SMC and significantly improve the correlation between the reflectance spectra and SMC.SMLR and PLSRmethods provide similar prediction accuracy.The PLSR-based model using a first-order reflectivity differential(R?)is more effective for estimating the SMC,and gave coefficient of determination(v2),root mean square errors of validation(RMSEV),and ratio of performance to inter-quartile distance(RPIQ)values of 0.946,1.347,and 6.328,respectively,for the calcareous purple soil,and 0.944,1.818,and 6.569,respectively,for the acidic purple soil.For neutral purple soil,the best prediction was obtained using the SMLR method with R?transformation,yieldingv2,RMSEV and RPIQ values of 0.973,0.888 and 8.791,respectively.In general,PLSR is more suitable than SMLR for estimating the SMC of purple soil.
文摘To explore the present status of Critical thinking and its relevant factors among undergraduates.A stratified random sampling was used to select 1013 undergraduates from 7 full-time colleges in Guangdong province.They were investigated with California Critical Thinking Disposition Inventory-Chinese Version(CTDI-CV)and a Self-Compiled Personal General Information Questionnaire.(1)The total score of CTDI-CV was(254.16±38.80).The undergraduates in the four levels of critical thinking of comprehensive strong,relatively strong,contradictory scope and serious opposition accounted for 1.78%,5.31%,87.4%and 5.51%of this group,respectively.(2)Multiple stepwise linear regression showed that the total score of CTDI-CV was positively correlated with the following 10 factors such as grade,family economic status,part-time experience,the teaching method used most commonly,like reading logic books,like reading reviews or essays,father’s warmth,mother’s warmth,openness and responsibility(β=.142 to.701,all P<.05).The following 5 factors such as father’s negation,father’s overprotection,mother’s negation,mother’s overprotection and neuroticism were negatively correlated with the total score of CTDI-CV(β=-.381 to-.616,all P<0.05).The overall level of critical thinking among undergraduates is relatively low.College Students’critical thinking may be related to many factors such as family rearing,school education and personal characteristics.
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 special project of the National Key Research and Development Program of China(Nos.2021YFC1809104 and 2018YFC1800104)。
文摘Estimating heavy metal(HM) distribution with high precision is the key to effectively preventing Chinese medicinal plants from being polluted by the native soil. A total of 44 surface soil samples were gathered to detect the concentrations of eight HMs(As, Hg, Cu, Cr, Ni, Zn, Pb, and Cd) in the herb growing area of Luanping County, northeastern Hebei Province, China. An absolute principal component score-multiple linear regression(APCS-MLR) model was used to quantify pollution source contributions to soil HMs. Furthermore, the source contribution rates and environmental data of each sampling point were simultaneously incorporated into a stepwise linear regression model to identify the crucial indicators for predicting soil HM spatial distributions. Results showed that 88% of Cu, 72% of Cr, and 72% of Ni came from natural sources;50% of Zn, 49% of Pb, and 59% of Cd were mainly caused by agricultural activities;and 44% of As and 56% of Hg originated from industrial activities. When three-type(natural, agricultural, and industrial) source contribution rates and environmental data were simultaneously incorporated into the stepwise linear regression model, the fitting accuracy was significantly improved and the model could explain 31%–86% of the total variance in soil HM concentrations. This study introduced three-type source contributions of each sampling point based on APCS-MLR analysis as new covariates to improve soil HM estimation precision, thus providing a new approach for predicting the spatial distribution of HMs using small sample sizes at the county scale.
基金financially supported by the National Natural Science Foundation of China(No.52176060,No.51876023)Dalian University of Technology 2021 Large-scale Instrument and Equipment Open Fund(No.DUTKFJJ2021041,No.DUTKFJJ2021044)。
文摘Air-Gap Diffusion Distillation(AGDD) is a new technology aiming at solving the problem of the safety of drinking water for residents in remote areas that uses a super hydrophilic porous medium as the hot channel and evaporation surface. In the experiment, it was found that the parameters of porous media have a significant influence on the desalination(evaporation) efficiency of AGDD. Although porous media are widely used as evaporation components, the factors affecting their evaporation efficiency are not fully understood. The evaporation process in super hydrophilic porous media is rarely discussed. A large number of experiments have been carried out based on AGDD. The introduction of statistical methods solves the problem that experiments cannot distinguish the contribution of complex parameters of porous media to evaporation efficiency. Stepwise regression analysis is used to reduce the dimensionality of the independent variables and construct regression equations(coefficient of determination R~2 reached 81.3%-96.8%). Evaporation flux correlations and dimensionless mass transfer correlations are established based on porous media parameters. We found that the surface evaporation of super hydrophilic porous media can be divided into three stages: diffusion evaporation, capillary evaporation, and thermal evaporation. The evaporation efficiency of these three stages is controlled by the vapor diffusion process resistance, capillary force, and energy supply. At low saturation, evaporation efficiency is limited by the resistance of the vapor diffusion process. The evaporation efficiency of the porous media is affected predominantly by the pore size, the specific surface area, porosity and the characteristic length. At high saturation, the evaporation efficiency becomes influenced primarily by the permeability. A small thickness and a high hydrophilicity also improve the evaporation efficiency.
