Rainfall is an important factor in estimating the event mean concentration （EMC） which is used to quantify the washed-off pollutant concentrations from non-point sources （NPSs）. Pollutant loads could also be calcu...Rainfall is an important factor in estimating the event mean concentration （EMC） which is used to quantify the washed-off pollutant concentrations from non-point sources （NPSs）. Pollutant loads could also be calculated using rainfall, catchment area and runoff coefficient. In this study, runoff quantity and quality data gathered from a 28-month monitoring conducted on the road and parking lot sites in Korea were evaluated using multiple linear regression （MLR） to develop equations for estimating pollutant loads and EMCs as a function of rainfall variables. The results revealed that total event rainfall and average rainfall intensity are possible predictors of pollutant loads. Overall, the models are indicators of the high uncertainties of NPSs; perhaps estimation of EMCs and loads could be accurately obtained by means of water quality sampling or a long term monitoring is needed to gather more data that can be used for the development of estimation models.展开更多
The purpose of this research is to explore the factors influencing the self-improvement process of museums in China and to conduct empirical analyses based on multiple linear regression models.As core institutions for...The purpose of this research is to explore the factors influencing the self-improvement process of museums in China and to conduct empirical analyses based on multiple linear regression models.As core institutions for inheriting and displaying cultural heritage and enhancing public cultural literacy,museums’self-improvement is of great significance in promoting cultural development,optimizing the supply of public cultural services,and enhancing social influence.This paper constructs a multiple linear regression model for the influencing factors of museum self-improvement by integrating several key variables,including emerging cultural and museum business(EF),institutional reform(SR),research and innovation level(RIL),management level(ML),and the museum cultural and creative industry(MCCI).The study employs scientific methods such as literature review,data collection,and data analysis to thoroughly explore the internal logic of museum operations and development.Through multiple linear regression analyses,it quantifies the specific influence and relative importance of each factor on the level of museum self-improvement.The results indicate that the management level(ML)is the dominant factor among the variables studied,exerting the most significant influence on museum self-improvement.Based on these empirical findings,this paper provides an in-depth analysis of the specific factors affecting museum self-improvement in China,offering solid theoretical support and practical guidance for the sustainable development of museums.展开更多
Mathematical modeling of economic indices is a challenging topic in crop production systems.The present study aimed to model the economic indices of mechanized and semimechanized rainfed wheat production systems using...Mathematical modeling of economic indices is a challenging topic in crop production systems.The present study aimed to model the economic indices of mechanized and semimechanized rainfed wheat production systems using various multiple linear regression models.The study area was Behshahr County located in the east of Mazandaran Province,Northern Iran.The statistical population included all wheat producers in Behshahr County in 2016/17 crop year.Five input variables were human labor,machinery,diesel fuel,chemical(chemical fertilizers and chemical pesticides)costs,and the income was considered to be the output.The results showed that the cost of wheat production in the semimechanized system was higher than that of the mechanized system.In both systems,the highest cost was related to agricultural machinery input.Moreover,seed cost was lower in the mechanized system than that of the semi-mechanized system.The net return indicator was 993.68$ha1 and 626.71$ha1 for the mechanized and semi-mechanized systems,respectively.The average benefit to cost ratio was 3.46 and 2.40 for the mechanized and semi-mechanized systems,respectively,demonstrating the greater profitability of the mechanized system.The results of the evaluation of five types of regression models including the Cobb-Douglas,linear,2FI,quadratic and pure-quadratic for the mechanized and semi-mechanized production systems indicated that in the developed Cobb-Douglas model,the R2-value was higher than that of the quadratic model while RMSE and MAPE of the quadratic model were determined to be smaller than that of the Cobb-Douglas model.Therefore,the best model to investigate the relationship between input costs and the income of wheat production in both mechanized and semi-mechanized systems was the quadratic model.展开更多
This paper selects seven indicators of financial revenue and housing sales price in recent 19 years in China,and uses SPSS and Excel to carry out descriptive statistics,independent sample t-test,correlation analysis a...This paper selects seven indicators of financial revenue and housing sales price in recent 19 years in China,and uses SPSS and Excel to carry out descriptive statistics,independent sample t-test,correlation analysis and regression analysis to comprehensively study the correlation between financial revenue and housing sales price in China,and establishes the relationship between financial revenue and housing sales price When the average selling price of commercial housing increases by one unit,the fiscal revenue will increase by 27.855 points.展开更多
Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the ...Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the forecast factors of forecast models.Secondly,the O_(3)-8h concentration in Baoding City in 2021 was predicted based on the constructed models of multiple linear regression(MLR),backward propagation neural network(BPNN),and auto regressive integrated moving average(ARIMA),and the predicted values were compared with the observed values to test their prediction effects.The results show that overall,the MLR,BPNN and ARIMA models were able to forecast the changing trend of O_(3)-8h concentration in Baoding in 2021,but the BPNN model gave better forecast results than the ARIMA and MLR models,especially for the prediction of the high values of O_(3)-8h concentration,and the correlation coefficients between the predicted values and the observed values were all higher than 0.9 during June-September.The mean error(ME),mean absolute error(MAE),and root mean square error(RMSE)of the predicted values and the observed values of daily O_(3)-8h concentration based on the BPNN model were 0.45,19.11 and 24.41μg/m 3,respectively,which were significantly better than those of the MLR and ARIMA models.The prediction effects of the MLR,BPNN and ARIMA models were the best at the pollution level,followed by the excellent level,and it was the worst at the good level.In comparison,the prediction effect of BPNN model was better than that of the MLR and ARIMA models as a whole,especially for the pollution and excellent levels.The TS scores of the BPNN model were all above 66%,and the PC values were above 86%.The BPNN model can forecast the changing trend of O_(3)concentration more accurately,and has a good practical application value,but at the same time,the predicted high values of O_(3)concentration should be appropriately increased according to error characteristics of the model.展开更多
In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not...In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging.Through a rigorous assessment,we explore the efficacy of eight regression models,bifurcated into linear and nonlinear groups,to accommodate the multifaceted nature of geological datasets.Our linear model suite encompasses the Standard Equation,Ridge Regression,Least Absolute Shrinkage and Selection Operator,and Elastic Net,each presenting distinct advantages.The Standard Equation serves as a foundational benchmark,whereas Ridge Regression implements penalty terms to counteract overfitting,thus bolstering model robustness in the presence of multicollinearity.The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models,enhancing their interpretability,while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator,offering a harmonized solution to model complexity and comprehensibility.On the nonlinear front,Gradient Descent,Kernel Ridge Regression,Support Vector Regression,and Piecewise Function-Fitting methods introduce innovative approaches.Gradient Descent assures computational efficiency in optimizing solutions,Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns,and Support Vector Regression is proficient in forecasting extremities,pivotal for exploration risk assessment.The Piecewise Function-Fitting approach,tailored for geological data,facilitates adaptable modeling of variable interrelations,accommodating abrupt data trend shifts.Our analysis identifies Ridge Regression,particularly when augmented by Piecewise Function-Fitting,as superior in recouping hydrocarbon losses,and underscoring its utility in resource quantification refinement.Meanwhile,Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A,evidencing its aptness for intricate geological structures.This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector.The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction,evaluation,and recovery.展开更多
The aim of this study was to assay the polyphenols,flavonoid,polyphenol oxidase and phenylalnine ammonialyase which were relative to the anthocyanins synthesis of purple corn. The optimization of multiple linear regre...The aim of this study was to assay the polyphenols,flavonoid,polyphenol oxidase and phenylalnine ammonialyase which were relative to the anthocyanins synthesis of purple corn. The optimization of multiple linear regression model of anthocyanins synthesis was y=4.383 86-0.205 45x1＋5.479 638x2＋0.195 575x4. According to standard partial regression coefficient testing,the result indicated that polyphenols content was negatively correlated with anthocyanins and the relative influence to anthocyanins synthesis was-42.7%; flavonoid content and activity of polyphenol oxidase were positively correlated with anthocyanins of purple corn and the relative influence to anthocyanins synthesis were 71.45% and 73.32% respectively. There was no positive correlation between the activity of phenylalnine ammonialyase and anthocyanins of purple corn. The establishment of multiple linear regression model of anthocyanins synthesis was to provide theory foundation of producing anthocyanins in laboratory.展开更多
Under the background of this era,green finance and the upgrading and optimization of industrial structure have become a hot research topic.The article focuses on Jiangsu Province,carefully explores the impact of green...Under the background of this era,green finance and the upgrading and optimization of industrial structure have become a hot research topic.The article focuses on Jiangsu Province,carefully explores the impact of green financial development on the upgrading and optimization of industrial structure and the real effect,collates and summarizes the theories of green finance and industrial structure at home and abroad,and carefully analyzes the development of green finance in Jiangsu Province,such as the gradual expansion of green credit scale,the characteristics of industrial structure,the change of the proportion of three industries,the development situation of emerging industries and so on.By means of econometrics,an empirical model covering Green Financial Development Indicators and industrial structure optimization indicators is established to do multiple linear regression analysis and stability test.The empirical results show that the development of green finance in Jiangsu plays an obvious positive role in the optimization and upgrading of industrial structure.Green finance is environmental protection,new energy and other green industries are given important financial support,which drives their scale expansion and technological innovation,and makes the industrial structure develop towards a higher level and a more reasonable direction.From this point of view,corresponding proposals are put forward to improve the policy incentive system,add green financial products,and strengthen the construction of green financial market.The purpose is to give better play to the advantages of green finance,accelerate the optimization and upgrading of industrial structure in Jiangsu,and provide theoretical basis and practical guidance for achieving green economic transformation and sustainable development.展开更多
The forest ecosystem plays a pivotal role in contributing greenhouse gases to the atmosphere.In order to characterize the temporal pattern of nitrous oxide(N_2O) emissions and identify the key factors affecting N_2O e...The forest ecosystem plays a pivotal role in contributing greenhouse gases to the atmosphere.In order to characterize the temporal pattern of nitrous oxide(N_2O) emissions and identify the key factors affecting N_2O emissions from a Masson pine forest in a hilly red-soil region in subtropical central China,we measured the N_2O emissions in Jinjing of Hunan Province using the static chambergas chromatographic method for 3 years(2010-2012) and analyzed the relationships between the N_2O fluxes and the environmental variables.Our results revealed that the N_2O fluxes over the 3 years varied from-36.0 to 296.7 μg N m^(-2) h^(-1),averaging 18.4±5.6 μg N m^(-2) h^(-1)(n=3).The average annual N_2O emissions were estimated to be 1.6±0.3 kg N ha^(-1) year^(-1).The N_2O fluxes exhibited clear intra-annual(seasonal) variations as they were higher in summers and lower in winters.Compared with other forest observations in the subtropics,N_2O emissions at our site were relatively high,possibly due to the high local dry/wet N deposition,and were mostly sensitive to variations in precipitation and soil ammonium N content.In this work,a multiple linear regression model was developed to determine the influence of environmental factors on N_2O emissions,in which a category predictor of "Season" was intentionally used to account for the seasonal variation of the N_2O fluxes.Such a model explained almost 40%of the total variation in daily N_2O emissions from the Masson pine forest soil studied(P<0.001).展开更多
Accumulation of heavy metals in soils poses a potential risk to plant production, which is related to availability of the metals in soil. The phytoavailability of metals is usually evaluated using extracting solutions...Accumulation of heavy metals in soils poses a potential risk to plant production, which is related to availability of the metals in soil. The phytoavailability of metals is usually evaluated using extracting solutions such as salts, acids or chelates. The purpose of this study was to identify the most significant soil parameters that can be used to predict the concentrations of acetic and citric acidextractable cadmium(Cd), lead(Pb) and zinc(Zn) in contaminated woody habitat topsoils. Multiple linear regression models were established using two analysis strategies and three sets of variables based on a dataset of 260 soil samples. The performance of these models was evaluated using statistical parameters. Cation exchange capacity, CaCO_3, organic matter, assimilated P, free Al oxide,sand and the total metal concentrations appeared to be the main soil parameters governing the solubility of Cd, Pb and Zn in acetic and citric acid solutions. The results strongly suggest that the metal solubility in extracting solutions is extractable concentrationdependent since models were overall improved by incorporating a change point. This change point detection method was a powerful tool for predicting extractable Cd, Pb and Zn. Suitable predictions of extractable Cd, Pb and Zn concentrations were obtained, with correlation coefficient(adjusted r) ranging from 0.80 to 0.99, given the high complexity of the woody habitat soils studied. Therefore,the predictive models can constitute a decision-making support tool for managing phytoremediation of contaminated soils, making recommendations to control the potential bioavailability of metals. The relationships between acetic and/or citric acid-extractable concentrations and the concentrations of metals into the aboveground parts of plants need to be predicted, in order to make their temporal monitoring easier.展开更多
Dew is an essential water resource for the survival and reproduction of organisms in arid and semi-arid regions.Yet estimating the dew amount and quantifying its long-term variation are challenging.In this study,we el...Dew is an essential water resource for the survival and reproduction of organisms in arid and semi-arid regions.Yet estimating the dew amount and quantifying its long-term variation are challenging.In this study,we elucidate the dew amount and its long-term variation in the Kunes River Valley,Northwest China,based on the measured daily dew amount and reconstructed values(using meteorological data from 1980 to 2021),respectively.Four key results were found:(1)the daily mean dew amount was 0.05 mm during the observation period(4 July-12 August and 13 September-7 October of 2021).In 35 d of the observation period(i.e.,73%of the observation period),the daily dew amount exceeded the threshold(>0.03 mm/d)for microorganisms;(2)air temperature,relative humidity,and wind speed had significant impacts on the daily dew amount based on the relationships between the measured dew amount and meteorological variables;(3)for estimating the daily dew amount,random forest(RF)model outperformed multiple linear regression(MLR)model given its larger R^(2) and lower MAE and RMSE;and(4)the dew amount during June-October and in each month did not vary significantly from 1980 to the beginning of the 21^(st) century.It then significantly decreased for about a decade,after it increased slightly from 2013 to 2021.For the whole meteorological period of 1980-2021,the dew amount decreased significantly during June-October and in July and September,and there was no significant variation in June,August,and October.Variation in the dew amount in the Kunes River Valley was mainly driven by relative humidity.This study illustrates that RF model can be used to reconstruct long-term variation in the dew amount,which provides valuable information for us to better understand the dew amount and its relationship with climate change.展开更多
<p> <span style="font-family:Verdana;">To address the drawbacks of the traditional Parker test in multivariate linear</span><span style="font-family:;" "=""> ...<p> <span style="font-family:Verdana;">To address the drawbacks of the traditional Parker test in multivariate linear</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">models:</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">the process is cumbersome and computationally intensive,</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">we propose a new heteroscedasticity test.</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">A new heteroskedasticity test is proposed using the fitted values of the samples as new explanatory variables, reconstructing the regression model, and giving a new heteroskedasticity test based on the significance test of the coefficients, it is also compared with the existing Parker test which is improved using the principal component idea. Numerical simulations and empirical analyses show that the improved Parker test with the fitted values of the samples proposed in this paper is superior.</span> </p>展开更多
Glacier response patterns at the catchment scale are highly heterogeneous and defined by a complex interplay of various dynamics and surface factors.Previous studies have explained heterogeneous responses in qualitati...Glacier response patterns at the catchment scale are highly heterogeneous and defined by a complex interplay of various dynamics and surface factors.Previous studies have explained heterogeneous responses in qualitative ways but quantitative assessment is lacking yet where an intrazone homogeneous climate assumption can be valid.Hence,in the current study,the reason for heterogeneous mass balance has been explained in quantitative methods using a multiple linear regression model in the Sikkim Himalayan region.At first,the topographical parameters are selected from previously published studies,then the most significant topographical and geomorphological parameters are selected with backward stepwise subset selection methods.Finally,the contributions of selected parameters are calculated by least square methods.The results show that,the magnitude of mass balance lies between-0.003±0.24 to-1.029±0.24 m.w.e.a^(-1) between 2000 and 2020 in the Sikkim Himalaya region.Also,the study shows that,out of the terminus type of the glacier,glacier area,debris cover,ice-mixed debris,slope,aspect,mean elevation,and snout elevation of the glaciers,only the terminus type and mean elevation of the glacier are significantly altering the glacier mass balance in the Sikkim Himalayan region.Mathematically,the mass loss is approximately 0.40 m.w.e.a^(-1) higher in the lake-terminating glaciers compared to the land-terminating glaciers in the same elevation zone.On the other hand,a thousand meters mean elevation drop is associated with 0.179 m.w.e.a-1of mass loss despite the terminus type of the glaciers.In the current study,the model using the terminus type of the glaciers and the mean elevation of the glaciers explains 76% of fluctuation of mass balance in the Sikkim Himalayan region.展开更多
Heavy floods occur frequently in the Senegal River Basin, causing catastrophic flooding downstream the river rating station of Bakel. Anticipating the occurrence of such phenomena is the only way to reduce the resulti...Heavy floods occur frequently in the Senegal River Basin, causing catastrophic flooding downstream the river rating station of Bakel. Anticipating the occurrence of such phenomena is the only way to reduce the resulting damages. Flood forecasting is a necessity. Flood forecasting plays also an important role in the implementation of flood management scenarios and in the protection of hydro electric structures. Many methods are applied. The most complete are based on the conservation laws of physics governing the free surface flow. These methods need a complete description of the geometry of the river and their implementation requires also huge investments. In practice the river basin can be considered as a system of inputs-outputs related by a transfer function. In this paper the authors first used a multiple linear regression model with constant parameters estimated by the ordinary least square method to simulate the propagation of the floods in the upstream part of the Senegal river basin. The authors then apply statistical and graphical criteria of goodness-of-fit to test the suitability of this model. Three procedures of parameters updating have then been added to this linear model： the Kalman filter method, the recursive least square method, and the stochastic gradient method The criteria of goodness-of-fit used above have shown that the stochastic gradient method, although more rudimentary, represents better the flood propagation in the head basin of the Senegal river upstream Bakel. This result is particularly interesting because data influenced by Manantali Dam are used.展开更多
This paper shows influence of gender equality on economy where it analyzed how gender equality in Europe has affected on the development of the frozen food industry and services related to childcare. The development o...This paper shows influence of gender equality on economy where it analyzed how gender equality in Europe has affected on the development of the frozen food industry and services related to childcare. The development of these industries has given a positive impulse to the development of the whole economy. In this analysis, it is used multiple regressions as one of the most important statistical methods. In the first part of this paper, it shows the connection among the growth of female employment, growth in frozen food expenditure and growth of GDP in United Kingdom. In the second part of paper, it shows the relationship among the growth of labor force participation of women, growth of number of kindergarten and growth of GDP in Hungary. To proof these relationships, it used a multiple regression model. This statistical model was tested by using the T schedule which showed that the model in both the analyses is correct. At the end of the paper, it presents that employment rate and GDP behaves in the same way in European Union. These analyses show that it is necessary to continue to strengthen gender equality if the policy makers want to achieve even greater economic growth. The issue of gender equality is a very important factor in creating employment policy, and statisticians should be more involved in process of employment policy and gender equality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
Kaolin/metakaolin-insulating ceramic components fabricated using direct ink writing(DIW)have important ap-plication prospects in architecture and aerospace.The accuracy of the entire process including the forming and ...Kaolin/metakaolin-insulating ceramic components fabricated using direct ink writing(DIW)have important ap-plication prospects in architecture and aerospace.The accuracy of the entire process including the forming and sintering accuracy of ceramics greatly limits the application scope,and high-accuracy ceramic samples can meet the usage requirements in many scenarios.The orthogonal experiment was designed with four process parame-ters,including nozzle internal diameter,filling rate,printing layer height/nozzle internal diameter,and printing speed,to investigate the evolution of the DIW forming accuracy,sintering shrinkage rate and surface roughness of metakaolin-based ceramics with different process parameters.The influence of each process parameter and its mechanism were analyzed to obtain the DIW parameters for high-accuracy metakaolin ceramics.Multiple linear regression models between the dimensional change rate,surface roughness,and process parameters of the ceramic samples were established and validated.The results show that comprehensively considering the forming accuracy of the ceramic green bodies,sintering shrinkage rate and surface roughness,the optimal DIW process parameters were a 0.41 mm nozzle internal diameter,100%filling rate,50%printing layer height/nozzle inter-nal diameter,and a 15 mm/s printing speed.Multiple linear regression models were developed for the process parameters and the printing accuracy,sintering shrinkage rate and surface roughness.The error rates between the theoretical results obtained by substituting the optimal process parameters into the multiple linear regression models and the actual results obtained by printing the samples with the optimal parameters were extremely small,all less than 0.8%.This verified the correctness and predictability of the multiple linear regression models.This work provides a reference basis for rapid fabrication of high-accuracy ceramics via DIW and accuracy prediction with different process parameters.展开更多
For a century or so, the Hong Kong Observatory （HKO） has been providing temperature forecast for the whole of Hong Kong with the HKO Headquarters as the reference location. In recent decades, due to spreading of pop...For a century or so, the Hong Kong Observatory （HKO） has been providing temperature forecast for the whole of Hong Kong with the HKO Headquarters as the reference location. In recent decades, due to spreading of population from the main urban center to satellite towns, there is an increasing demand for regional temperature forecasts. To support such provision, the HKO has developed a regression model to provide objective guidance to forecasters in formulating forecasts of maximum and minimum temperatures for the next day at various locations in Hong Kong. In this paper, the regression model is presented, together with the assessment of its performance. Based on the verification of one year of forecasts, it is found that the root mean square errors （RMSEs） of maximum （minimum） temperature forecasts are from about 1.3 to 2.1 （1.1 to 1.4） degrees, respectively. The regression model is shown to have generally out-performed the operational regional spectral model then operated by HKO. Regional temperature forecast methods of other meteorological or research centers are also surveyed. Equipped with the regression model, the HKO has launched an online regional temperature forecast service for the next day in Hong Kong since March 2008.展开更多
基金provided by the Korean Ministry of Environment and Eco Star Project
文摘Rainfall is an important factor in estimating the event mean concentration （EMC） which is used to quantify the washed-off pollutant concentrations from non-point sources （NPSs）. Pollutant loads could also be calculated using rainfall, catchment area and runoff coefficient. In this study, runoff quantity and quality data gathered from a 28-month monitoring conducted on the road and parking lot sites in Korea were evaluated using multiple linear regression （MLR） to develop equations for estimating pollutant loads and EMCs as a function of rainfall variables. The results revealed that total event rainfall and average rainfall intensity are possible predictors of pollutant loads. Overall, the models are indicators of the high uncertainties of NPSs; perhaps estimation of EMCs and loads could be accurately obtained by means of water quality sampling or a long term monitoring is needed to gather more data that can be used for the development of estimation models.
基金2024 Guangdong Philosophy and Social Science Planning Discipline Co-construction Project“Study on the Measurement of Economic Benefits and Path of High-Quality Development of Museums in Guangdong Province”(Project No.GD24XYS045)Key Project of the Social Sciences Division of Shenzhen Polytechnic University“Research on Strategies for Enhancing the Effectiveness of Non-State-Owned Museums in Shenzhen”(Project No.20240105)+1 种基金Shenzhen Polytechnic University’s Platform Construction Project“SZPU-Fangzhi Technology AI New Media R&D Centre”(Project No:602331019PQ)Open-ended Project of the Global Urban Civilization Model Research Institute of Southern University of Science and Technology in 2024,“Research on the Efficiency Enhancement Strategy of Non State owned Museums in Shenzhen from the Perspective of Urban Civilization Construction”(Project No.IGUC24C011)。
文摘The purpose of this research is to explore the factors influencing the self-improvement process of museums in China and to conduct empirical analyses based on multiple linear regression models.As core institutions for inheriting and displaying cultural heritage and enhancing public cultural literacy,museums’self-improvement is of great significance in promoting cultural development,optimizing the supply of public cultural services,and enhancing social influence.This paper constructs a multiple linear regression model for the influencing factors of museum self-improvement by integrating several key variables,including emerging cultural and museum business(EF),institutional reform(SR),research and innovation level(RIL),management level(ML),and the museum cultural and creative industry(MCCI).The study employs scientific methods such as literature review,data collection,and data analysis to thoroughly explore the internal logic of museum operations and development.Through multiple linear regression analyses,it quantifies the specific influence and relative importance of each factor on the level of museum self-improvement.The results indicate that the management level(ML)is the dominant factor among the variables studied,exerting the most significant influence on museum self-improvement.Based on these empirical findings,this paper provides an in-depth analysis of the specific factors affecting museum self-improvement in China,offering solid theoretical support and practical guidance for the sustainable development of museums.
文摘Mathematical modeling of economic indices is a challenging topic in crop production systems.The present study aimed to model the economic indices of mechanized and semimechanized rainfed wheat production systems using various multiple linear regression models.The study area was Behshahr County located in the east of Mazandaran Province,Northern Iran.The statistical population included all wheat producers in Behshahr County in 2016/17 crop year.Five input variables were human labor,machinery,diesel fuel,chemical(chemical fertilizers and chemical pesticides)costs,and the income was considered to be the output.The results showed that the cost of wheat production in the semimechanized system was higher than that of the mechanized system.In both systems,the highest cost was related to agricultural machinery input.Moreover,seed cost was lower in the mechanized system than that of the semi-mechanized system.The net return indicator was 993.68$ha1 and 626.71$ha1 for the mechanized and semi-mechanized systems,respectively.The average benefit to cost ratio was 3.46 and 2.40 for the mechanized and semi-mechanized systems,respectively,demonstrating the greater profitability of the mechanized system.The results of the evaluation of five types of regression models including the Cobb-Douglas,linear,2FI,quadratic and pure-quadratic for the mechanized and semi-mechanized production systems indicated that in the developed Cobb-Douglas model,the R2-value was higher than that of the quadratic model while RMSE and MAPE of the quadratic model were determined to be smaller than that of the Cobb-Douglas model.Therefore,the best model to investigate the relationship between input costs and the income of wheat production in both mechanized and semi-mechanized systems was the quadratic model.
基金Thank you for your valuable comments and suggestions.This research was supported by Yunnan applied basic research project(NO.2017FD150)Chuxiong Normal University General Research Project(NO.XJYB2001).
文摘This paper selects seven indicators of financial revenue and housing sales price in recent 19 years in China,and uses SPSS and Excel to carry out descriptive statistics,independent sample t-test,correlation analysis and regression analysis to comprehensively study the correlation between financial revenue and housing sales price in China,and establishes the relationship between financial revenue and housing sales price When the average selling price of commercial housing increases by one unit,the fiscal revenue will increase by 27.855 points.
基金the Project of the Key Open Laboratory of Atmospheric Detection,China Meteorological Administration(2023KLAS02M)the Second Batch of Science and Technology Project of China Meteorological Administration("Jiebangguashuai"):the Research and Development of Short-term and Near-term Warning Products for Severe Convective Weather in Beijing-Tianjin-Hebei Region(CMAJBGS202307).
文摘Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the forecast factors of forecast models.Secondly,the O_(3)-8h concentration in Baoding City in 2021 was predicted based on the constructed models of multiple linear regression(MLR),backward propagation neural network(BPNN),and auto regressive integrated moving average(ARIMA),and the predicted values were compared with the observed values to test their prediction effects.The results show that overall,the MLR,BPNN and ARIMA models were able to forecast the changing trend of O_(3)-8h concentration in Baoding in 2021,but the BPNN model gave better forecast results than the ARIMA and MLR models,especially for the prediction of the high values of O_(3)-8h concentration,and the correlation coefficients between the predicted values and the observed values were all higher than 0.9 during June-September.The mean error(ME),mean absolute error(MAE),and root mean square error(RMSE)of the predicted values and the observed values of daily O_(3)-8h concentration based on the BPNN model were 0.45,19.11 and 24.41μg/m 3,respectively,which were significantly better than those of the MLR and ARIMA models.The prediction effects of the MLR,BPNN and ARIMA models were the best at the pollution level,followed by the excellent level,and it was the worst at the good level.In comparison,the prediction effect of BPNN model was better than that of the MLR and ARIMA models as a whole,especially for the pollution and excellent levels.The TS scores of the BPNN model were all above 66%,and the PC values were above 86%.The BPNN model can forecast the changing trend of O_(3)concentration more accurately,and has a good practical application value,but at the same time,the predicted high values of O_(3)concentration should be appropriately increased according to error characteristics of the model.
文摘In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging.Through a rigorous assessment,we explore the efficacy of eight regression models,bifurcated into linear and nonlinear groups,to accommodate the multifaceted nature of geological datasets.Our linear model suite encompasses the Standard Equation,Ridge Regression,Least Absolute Shrinkage and Selection Operator,and Elastic Net,each presenting distinct advantages.The Standard Equation serves as a foundational benchmark,whereas Ridge Regression implements penalty terms to counteract overfitting,thus bolstering model robustness in the presence of multicollinearity.The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models,enhancing their interpretability,while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator,offering a harmonized solution to model complexity and comprehensibility.On the nonlinear front,Gradient Descent,Kernel Ridge Regression,Support Vector Regression,and Piecewise Function-Fitting methods introduce innovative approaches.Gradient Descent assures computational efficiency in optimizing solutions,Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns,and Support Vector Regression is proficient in forecasting extremities,pivotal for exploration risk assessment.The Piecewise Function-Fitting approach,tailored for geological data,facilitates adaptable modeling of variable interrelations,accommodating abrupt data trend shifts.Our analysis identifies Ridge Regression,particularly when augmented by Piecewise Function-Fitting,as superior in recouping hydrocarbon losses,and underscoring its utility in resource quantification refinement.Meanwhile,Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A,evidencing its aptness for intricate geological structures.This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector.The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction,evaluation,and recovery.
文摘The aim of this study was to assay the polyphenols,flavonoid,polyphenol oxidase and phenylalnine ammonialyase which were relative to the anthocyanins synthesis of purple corn. The optimization of multiple linear regression model of anthocyanins synthesis was y=4.383 86-0.205 45x1＋5.479 638x2＋0.195 575x4. According to standard partial regression coefficient testing,the result indicated that polyphenols content was negatively correlated with anthocyanins and the relative influence to anthocyanins synthesis was-42.7%; flavonoid content and activity of polyphenol oxidase were positively correlated with anthocyanins of purple corn and the relative influence to anthocyanins synthesis were 71.45% and 73.32% respectively. There was no positive correlation between the activity of phenylalnine ammonialyase and anthocyanins of purple corn. The establishment of multiple linear regression model of anthocyanins synthesis was to provide theory foundation of producing anthocyanins in laboratory.
基金The Impact of Digital Economy on Green Development Efficiency.2025 Nanjing University of Science and Technology Zijin College Campus Level Scientific Research Project(Project No.:2025ZXSK0401011)。
文摘Under the background of this era,green finance and the upgrading and optimization of industrial structure have become a hot research topic.The article focuses on Jiangsu Province,carefully explores the impact of green financial development on the upgrading and optimization of industrial structure and the real effect,collates and summarizes the theories of green finance and industrial structure at home and abroad,and carefully analyzes the development of green finance in Jiangsu Province,such as the gradual expansion of green credit scale,the characteristics of industrial structure,the change of the proportion of three industries,the development situation of emerging industries and so on.By means of econometrics,an empirical model covering Green Financial Development Indicators and industrial structure optimization indicators is established to do multiple linear regression analysis and stability test.The empirical results show that the development of green finance in Jiangsu plays an obvious positive role in the optimization and upgrading of industrial structure.Green finance is environmental protection,new energy and other green industries are given important financial support,which drives their scale expansion and technological innovation,and makes the industrial structure develop towards a higher level and a more reasonable direction.From this point of view,corresponding proposals are put forward to improve the policy incentive system,add green financial products,and strengthen the construction of green financial market.The purpose is to give better play to the advantages of green finance,accelerate the optimization and upgrading of industrial structure in Jiangsu,and provide theoretical basis and practical guidance for achieving green economic transformation and sustainable development.
基金supported by the National Basic Research Program of China(No.2012CB417105)the International Partnership Program for Creative Research Team of Chinese Academy of Sciences/the State Administration of Foreign Experts Affairs of China(Nos.KZCX2-YW-T07 and 20100491005-8)the 100 Talents Programme of Chinese Academy of Sciences
文摘The forest ecosystem plays a pivotal role in contributing greenhouse gases to the atmosphere.In order to characterize the temporal pattern of nitrous oxide(N_2O) emissions and identify the key factors affecting N_2O emissions from a Masson pine forest in a hilly red-soil region in subtropical central China,we measured the N_2O emissions in Jinjing of Hunan Province using the static chambergas chromatographic method for 3 years(2010-2012) and analyzed the relationships between the N_2O fluxes and the environmental variables.Our results revealed that the N_2O fluxes over the 3 years varied from-36.0 to 296.7 μg N m^(-2) h^(-1),averaging 18.4±5.6 μg N m^(-2) h^(-1)(n=3).The average annual N_2O emissions were estimated to be 1.6±0.3 kg N ha^(-1) year^(-1).The N_2O fluxes exhibited clear intra-annual(seasonal) variations as they were higher in summers and lower in winters.Compared with other forest observations in the subtropics,N_2O emissions at our site were relatively high,possibly due to the high local dry/wet N deposition,and were mostly sensitive to variations in precipitation and soil ammonium N content.In this work,a multiple linear regression model was developed to determine the influence of environmental factors on N_2O emissions,in which a category predictor of "Season" was intentionally used to account for the seasonal variation of the N_2O fluxes.Such a model explained almost 40%of the total variation in daily N_2O emissions from the Masson pine forest soil studied(P<0.001).
基金“Agence de l’Environnement et de la Maitrise de l’Energie”(ADEME)“Agence Nationale pour la Recherche”(ANR)for their financial support of the STARTT programmeADEME for the financial support of the PHYTENER programme
文摘Accumulation of heavy metals in soils poses a potential risk to plant production, which is related to availability of the metals in soil. The phytoavailability of metals is usually evaluated using extracting solutions such as salts, acids or chelates. The purpose of this study was to identify the most significant soil parameters that can be used to predict the concentrations of acetic and citric acidextractable cadmium(Cd), lead(Pb) and zinc(Zn) in contaminated woody habitat topsoils. Multiple linear regression models were established using two analysis strategies and three sets of variables based on a dataset of 260 soil samples. The performance of these models was evaluated using statistical parameters. Cation exchange capacity, CaCO_3, organic matter, assimilated P, free Al oxide,sand and the total metal concentrations appeared to be the main soil parameters governing the solubility of Cd, Pb and Zn in acetic and citric acid solutions. The results strongly suggest that the metal solubility in extracting solutions is extractable concentrationdependent since models were overall improved by incorporating a change point. This change point detection method was a powerful tool for predicting extractable Cd, Pb and Zn. Suitable predictions of extractable Cd, Pb and Zn concentrations were obtained, with correlation coefficient(adjusted r) ranging from 0.80 to 0.99, given the high complexity of the woody habitat soils studied. Therefore,the predictive models can constitute a decision-making support tool for managing phytoremediation of contaminated soils, making recommendations to control the potential bioavailability of metals. The relationships between acetic and/or citric acid-extractable concentrations and the concentrations of metals into the aboveground parts of plants need to be predicted, in order to make their temporal monitoring easier.
基金supported by the National Natural Science Foundation of China (41901048)the Project of State Key Laboratory of Desert and Oasis Ecology,Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences (E151030101)+1 种基金the Project of National Cryosphere Desert Data Center of China (2021kf02)the Youth Innovation Promotion Association of the Chinese Academy of Sciences (2021438)
文摘Dew is an essential water resource for the survival and reproduction of organisms in arid and semi-arid regions.Yet estimating the dew amount and quantifying its long-term variation are challenging.In this study,we elucidate the dew amount and its long-term variation in the Kunes River Valley,Northwest China,based on the measured daily dew amount and reconstructed values(using meteorological data from 1980 to 2021),respectively.Four key results were found:(1)the daily mean dew amount was 0.05 mm during the observation period(4 July-12 August and 13 September-7 October of 2021).In 35 d of the observation period(i.e.,73%of the observation period),the daily dew amount exceeded the threshold(>0.03 mm/d)for microorganisms;(2)air temperature,relative humidity,and wind speed had significant impacts on the daily dew amount based on the relationships between the measured dew amount and meteorological variables;(3)for estimating the daily dew amount,random forest(RF)model outperformed multiple linear regression(MLR)model given its larger R^(2) and lower MAE and RMSE;and(4)the dew amount during June-October and in each month did not vary significantly from 1980 to the beginning of the 21^(st) century.It then significantly decreased for about a decade,after it increased slightly from 2013 to 2021.For the whole meteorological period of 1980-2021,the dew amount decreased significantly during June-October and in July and September,and there was no significant variation in June,August,and October.Variation in the dew amount in the Kunes River Valley was mainly driven by relative humidity.This study illustrates that RF model can be used to reconstruct long-term variation in the dew amount,which provides valuable information for us to better understand the dew amount and its relationship with climate change.
文摘<p> <span style="font-family:Verdana;">To address the drawbacks of the traditional Parker test in multivariate linear</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">models:</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">the process is cumbersome and computationally intensive,</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">we propose a new heteroscedasticity test.</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">A new heteroskedasticity test is proposed using the fitted values of the samples as new explanatory variables, reconstructing the regression model, and giving a new heteroskedasticity test based on the significance test of the coefficients, it is also compared with the existing Parker test which is improved using the principal component idea. Numerical simulations and empirical analyses show that the improved Parker test with the fitted values of the samples proposed in this paper is superior.</span> </p>
文摘Glacier response patterns at the catchment scale are highly heterogeneous and defined by a complex interplay of various dynamics and surface factors.Previous studies have explained heterogeneous responses in qualitative ways but quantitative assessment is lacking yet where an intrazone homogeneous climate assumption can be valid.Hence,in the current study,the reason for heterogeneous mass balance has been explained in quantitative methods using a multiple linear regression model in the Sikkim Himalayan region.At first,the topographical parameters are selected from previously published studies,then the most significant topographical and geomorphological parameters are selected with backward stepwise subset selection methods.Finally,the contributions of selected parameters are calculated by least square methods.The results show that,the magnitude of mass balance lies between-0.003±0.24 to-1.029±0.24 m.w.e.a^(-1) between 2000 and 2020 in the Sikkim Himalaya region.Also,the study shows that,out of the terminus type of the glacier,glacier area,debris cover,ice-mixed debris,slope,aspect,mean elevation,and snout elevation of the glaciers,only the terminus type and mean elevation of the glacier are significantly altering the glacier mass balance in the Sikkim Himalayan region.Mathematically,the mass loss is approximately 0.40 m.w.e.a^(-1) higher in the lake-terminating glaciers compared to the land-terminating glaciers in the same elevation zone.On the other hand,a thousand meters mean elevation drop is associated with 0.179 m.w.e.a-1of mass loss despite the terminus type of the glaciers.In the current study,the model using the terminus type of the glaciers and the mean elevation of the glaciers explains 76% of fluctuation of mass balance in the Sikkim Himalayan region.
文摘Heavy floods occur frequently in the Senegal River Basin, causing catastrophic flooding downstream the river rating station of Bakel. Anticipating the occurrence of such phenomena is the only way to reduce the resulting damages. Flood forecasting is a necessity. Flood forecasting plays also an important role in the implementation of flood management scenarios and in the protection of hydro electric structures. Many methods are applied. The most complete are based on the conservation laws of physics governing the free surface flow. These methods need a complete description of the geometry of the river and their implementation requires also huge investments. In practice the river basin can be considered as a system of inputs-outputs related by a transfer function. In this paper the authors first used a multiple linear regression model with constant parameters estimated by the ordinary least square method to simulate the propagation of the floods in the upstream part of the Senegal river basin. The authors then apply statistical and graphical criteria of goodness-of-fit to test the suitability of this model. Three procedures of parameters updating have then been added to this linear model： the Kalman filter method, the recursive least square method, and the stochastic gradient method The criteria of goodness-of-fit used above have shown that the stochastic gradient method, although more rudimentary, represents better the flood propagation in the head basin of the Senegal river upstream Bakel. This result is particularly interesting because data influenced by Manantali Dam are used.
文摘This paper shows influence of gender equality on economy where it analyzed how gender equality in Europe has affected on the development of the frozen food industry and services related to childcare. The development of these industries has given a positive impulse to the development of the whole economy. In this analysis, it is used multiple regressions as one of the most important statistical methods. In the first part of this paper, it shows the connection among the growth of female employment, growth in frozen food expenditure and growth of GDP in United Kingdom. In the second part of paper, it shows the relationship among the growth of labor force participation of women, growth of number of kindergarten and growth of GDP in Hungary. To proof these relationships, it used a multiple regression model. This statistical model was tested by using the T schedule which showed that the model in both the analyses is correct. At the end of the paper, it presents that employment rate and GDP behaves in the same way in European Union. These analyses show that it is necessary to continue to strengthen gender equality if the policy makers want to achieve even greater economic growth. The issue of gender equality is a very important factor in creating employment policy, and statisticians should be more involved in process of employment policy and gender equality
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.
基金supported by National Key Research and Develop-ment Program of China(Grant.No.2022YFB4602502)Free Exploration Basic Research Project of Local Science and Technology Development Funds Guided by the Central Government of China(Grant.Nos.2021Szvup158,2021Szvup159)+5 种基金National Nature Science Foundation of China(Grant.No.52375395)Shenzhen Fundamental Research Program of China(Grant.No.JCYJ20220818102601004)Research Project of the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(Grant.No.CUG2106346)Science and Technology Reveal System Project of Hubei Province of China(Grant.No.2021BEC010)Research Project of State Key Laboratory of Materials Processing and Die&Mould Technology of China(Grant.No.P2021-020)the Scientific Compass Testing Institute for the SEM images.
文摘Kaolin/metakaolin-insulating ceramic components fabricated using direct ink writing(DIW)have important ap-plication prospects in architecture and aerospace.The accuracy of the entire process including the forming and sintering accuracy of ceramics greatly limits the application scope,and high-accuracy ceramic samples can meet the usage requirements in many scenarios.The orthogonal experiment was designed with four process parame-ters,including nozzle internal diameter,filling rate,printing layer height/nozzle internal diameter,and printing speed,to investigate the evolution of the DIW forming accuracy,sintering shrinkage rate and surface roughness of metakaolin-based ceramics with different process parameters.The influence of each process parameter and its mechanism were analyzed to obtain the DIW parameters for high-accuracy metakaolin ceramics.Multiple linear regression models between the dimensional change rate,surface roughness,and process parameters of the ceramic samples were established and validated.The results show that comprehensively considering the forming accuracy of the ceramic green bodies,sintering shrinkage rate and surface roughness,the optimal DIW process parameters were a 0.41 mm nozzle internal diameter,100%filling rate,50%printing layer height/nozzle inter-nal diameter,and a 15 mm/s printing speed.Multiple linear regression models were developed for the process parameters and the printing accuracy,sintering shrinkage rate and surface roughness.The error rates between the theoretical results obtained by substituting the optimal process parameters into the multiple linear regression models and the actual results obtained by printing the samples with the optimal parameters were extremely small,all less than 0.8%.This verified the correctness and predictability of the multiple linear regression models.This work provides a reference basis for rapid fabrication of high-accuracy ceramics via DIW and accuracy prediction with different process parameters.
文摘For a century or so, the Hong Kong Observatory （HKO） has been providing temperature forecast for the whole of Hong Kong with the HKO Headquarters as the reference location. In recent decades, due to spreading of population from the main urban center to satellite towns, there is an increasing demand for regional temperature forecasts. To support such provision, the HKO has developed a regression model to provide objective guidance to forecasters in formulating forecasts of maximum and minimum temperatures for the next day at various locations in Hong Kong. In this paper, the regression model is presented, together with the assessment of its performance. Based on the verification of one year of forecasts, it is found that the root mean square errors （RMSEs） of maximum （minimum） temperature forecasts are from about 1.3 to 2.1 （1.1 to 1.4） degrees, respectively. The regression model is shown to have generally out-performed the operational regional spectral model then operated by HKO. Regional temperature forecast methods of other meteorological or research centers are also surveyed. Equipped with the regression model, the HKO has launched an online regional temperature forecast service for the next day in Hong Kong since March 2008.
