Backgrounds:Evaluating the growth performance of pigs in real-time is laborious and expensive,thus mathematical models based on easily accessible variables are developed.Multiple regression(MR)is the most widely used ...Backgrounds:Evaluating the growth performance of pigs in real-time is laborious and expensive,thus mathematical models based on easily accessible variables are developed.Multiple regression(MR)is the most widely used tool to build prediction models in swine nutrition,while the artificial neural networks(ANN)model is reported to be more accurate than MR model in prediction performance.Therefore,the potential of ANN models in predicting the growth performance of pigs was evaluated and compared with MR models in this study.Results:Body weight(BW),net energy(NE)intake,standardized ileal digestible lysine(SID Lys)intake,and their quadratic terms were selected as input variables to predict ADG and F/G among 10 candidate variables.In the training phase,MR models showed high accuracy in both ADG and F/G prediction(R^(2)_(ADG)=0.929,R^(2)_(F/G)=0.886)while ANN models with 4,6 neurons and radial basis activation function yielded the best performance in ADG and F/G prediction(R^(2)_(ADG)=0.964,R^(2)_(F/G)=0.932).In the testing phase,these ANN models showed better accuracy in ADG prediction(CCC:0.976 vs.0.861,R^(2):0.951 vs.0.584),and F/G prediction(CCC:0.952 vs.0.900,R^(2):0.905 vs.0.821)compared with the MR models.Meanwhile,the“over-fitting”occurred in MR models but not in ANN models.On validation data from the animal trial,ANN models exhibited superiority over MR models in both ADG and F/G prediction(P<0.01).Moreover,the growth stages have a significant effect on the prediction accuracy of the models.Conclusion:Body weight,NE intake and SID Lys intake can be used as input variables to predict the growth performance of growing-finishing pigs,with trained ANN models are more flexible and accurate than MR models.Therefore,it is promising to use ANN models in related swine nutrition studies in the future.展开更多
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
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
During underground coal gasification （UCG）, whereby coal is converted to syngas in situ, a cavity is formed in the coal seam. The cavity growth rate （CGR） or the moving rate of the gasification face is affected by...During underground coal gasification （UCG）, whereby coal is converted to syngas in situ, a cavity is formed in the coal seam. The cavity growth rate （CGR） or the moving rate of the gasification face is affected by controllable （operation pressure, gasification time, geometry of UCG panel） and uncontrollable （coal seam properties） factors. The CGR is usually predicted by mathematical models and laboratory experiments, which are time consuming, cumbersome and expensive. In this paper, a new simple model for CGR is developed using non-linear regression analysis, based on data from 1 l UCG field trials. The empirical model compares satisfactorily with Perkins model and can reliably predict CGR.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
Amid global climate change, rising levels of nitrogen(N) deposition have attracted considerable attention for their potential effects on the carbon cycle of terrestrial ecosystems. The desert steppes are a crucial yet...Amid global climate change, rising levels of nitrogen(N) deposition have attracted considerable attention for their potential effects on the carbon cycle of terrestrial ecosystems. The desert steppes are a crucial yet vulnerable ecosystem in arid areas, but their response to the combination of N addition and precipitation(a crucial factor in arid areas) remains underexplored. This study systematically explored the impact of N addition and precipitation on net ecosystem exchange(NEE) in a desert steppe in northern China. Specifically, we conducted a 2-a experiment from 2022 to 2023 with eight N addition treatments in the Urat desert steppe of Inner Mongolia Autonomous Region, China, to examine changes in NEE and explore its driving factors. The structural equation model(SEM) and multiple regression model were applied to determine the relationship of NEE with plant community characteristics and soil physical-chemical properties. Statistical results showed that N addition has no significant effect on NEE.However, it has a significant impact on the functional traits of desert steppe plant communities. SEM results further revealed that N addition has no significant effect on NEE in the desert steppe, whereas annual precipitation can influence NEE variations. The multiple regression model analysis indicated that plant functional traits play an important role in explaining the changes in NEE, accounting for 62.15% of the variation in NEE. In addition, plant height, as an important plant functional trait indicator, shows stronger reliability in predicting the changes in NEE and becomes a more promising predictor. These findings provide valuable insights into the complex ecological mechanisms governing plant community responses to precipitation and nutrient availability in the arid desert steppes, contributing to the improved monitoring and prediction of desert steppe ecosystem responses to global climate change.展开更多
The study on the factors affecting the performance of small and medium enterprises(SMEs)in Lao PDR aims at firstly investigating the general characteristics of SMEs in Lao PDR,and secondly defining the factors affecti...The study on the factors affecting the performance of small and medium enterprises(SMEs)in Lao PDR aims at firstly investigating the general characteristics of SMEs in Lao PDR,and secondly defining the factors affecting business performance of small and medium enterprises in Lao PDR.The secondary data(2018)surveyed by World Bank enterprises were employed.The descriptive statistics were employed in order to see whether independent and dependent variables have impacts on SMEs’business performance.It was found that all small and medium enterprises were from family-based enterprises with no competition and no application of science and technology.It also revealed that the experiences of managers,services,duration of business implementation,number of laborers,number of trainings,export,access to finance,and innovation have significant relationship with SMEs’business performance.Whereas,it was found that gender of managers,size of SMEs,location,type of enterprises:manufacturing and retail,have no significant relationship with SMEs’business performance in Lao PDR.展开更多
The heteroepitaxy of diamond films has received widespread attention;however,its application remains limited owing to the mismatch in properties and structure between diamond and heterogeneous substrates.In this study...The heteroepitaxy of diamond films has received widespread attention;however,its application remains limited owing to the mismatch in properties and structure between diamond and heterogeneous substrates.In this study,diamond films were successfully synthesized on high-entropy alloys(HEAs)substrates using microwave plasma chemical vapor deposition.The resulting diamond films were continuous,uniform,and adhered to the HEAs substrates.The mixed carbides were identified using X-ray diffraction,and the quality of the diamond films was examined using Raman spectroscopy.Moreover,the corrosion test revealed that the diamond/TiZrHfMo samples had excellent electrochemical stability and corrosion resistance with a corrosion potential value of-0.169 V in a 3.5wt%NaCl solution.A multiple regression model was established to evaluate the effects of the structure and growth parameters,which confirmed that the mixing entropy significantly affected the grain size and corrosion properties.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
An accurate assessment of the property value is very important to make a deal, property tax, and mortgage for loan. The mass appraisal system has been developed in some foreign countries, especially in American for a ...An accurate assessment of the property value is very important to make a deal, property tax, and mortgage for loan. The mass appraisal system has been developed in some foreign countries, especially in American for a long time. In Taiwan, we still have few experiences in using computer-assisted mass appraisal system, especially using artificial neural network （ANN）. This article has two objectives： （1） to illustrate application of ANN to the Kaohsiung property market by the method of back-propagation. The study is based on the properties data of sales price, we also use multiple regressions in the same data; （2） to evaluate the performance of two models by using the mean absolute percentage error （MAPE） and hit ratio （HR）. This paper finds that using artificial neural network （ANN） is able to overcome multiple regressions＇ methodological problems and also get better performance than multiple regression model （MRA）. These results are useful in helping local government to assess their assessment value.展开更多
This study developed empirical-mathematical models to predict the California Bearing Ratio (CBR) using soil index properties in Ogbia-Nembe road in the Niger Delta region of Nigeria. The determination of CBR of soil i...This study developed empirical-mathematical models to predict the California Bearing Ratio (CBR) using soil index properties in Ogbia-Nembe road in the Niger Delta region of Nigeria. The determination of CBR of soil is a laborious operation that requires a longer time and materials leading to increased cost and schedule;this can be reduced by adopting an empirical-mathematical model that can predict the CBR using other simpler soil index properties such as Plastic Limit (PL), the Liquid Limit (LL), the Plasticity Index (PI) and the Moisture Content (MC), which are less laborious and take lesser time to obtain. Thirteen models were developed to understand the relationship between these soil index properties: the independent variable and the California Bearing Ratio (CBR): the dependent variable;Six linear, Six quadratic and One multiple linear regression models were developed for this relationship. Analysis of variance (ANOVA) on the thirteen models showed that the Optimum Moisture Content (OMC) and the Maximum Dry Density (MDD) are better independent variables for the prediction of the CBR value of Ogbia-Nembe soil generating a quadratic model and a multiple linear regression model having a better coefficient of determination R<sup>2</sup> = 0.96 and 0.94 respectively, mean square error (MSE) of 0.74 and 1.152 respectively with Root mean square errors of 0.861 and 1.073 accordingly. These models were used to predict the CBR of the soil. The CBR values predicted by the model were further compared with those of the actual experimental test and found to be relatively consistent with minimal variance. This establishes that CBR of any soil can be predicted from the Index Property of the soil and this is more economical and takes lesser time and can be universally adopted for soil investigation.展开更多
The aim of this study was to model the Undrained Shear Strength (USS) of soil found in the coastal region of the Niger Delta in Nigeria with some soil properties. The undrained shear strength (USS) is a key parameter ...The aim of this study was to model the Undrained Shear Strength (USS) of soil found in the coastal region of the Niger Delta in Nigeria with some soil properties. The undrained shear strength (USS) is a key parameter needed for most geotechnical/structural designs. Accurate determination of the USS of soft clays can be challenging to obtain in the laboratory due to the difficulty in remoulding the clay to its in-situ conditions before testing and more accurate test such as Cone Penetration test (CPT) can be quite expensive. This study was carried out at Escravos site which is located in Delta state, Nigeria. Three Boreholes were drilled and soil samples were collected at 0.75 m intervals up to a depth of 45 m. Laboratory tests were used to obtain the moisture content, bulk unit weight, liquid and plastic limit, while CPT was used in obtaining the undrained shear strength. Classification of the soil samples was done by adopting the Unified Soil Classification System and various models relating the USS with the soil properties were developed. The result showed that most of the soils at Escravos site were predominately inorganic clay of high plasticity which are problematic due to the expansion and shrinking nature of this type of soil. The model developed showed that the soil properties that gave the best fit with the USS were the moisture content and effective stress of the soil. The coefficient of determination (R<sup>2</sup>) and the root mean square error (RMSE) obtained for this model were 0.805 and 6.37 KN/m<sup>2</sup>, respectively.展开更多
Understanding the strength characteristics and deformation behaviour of the tunnel surrounding rock in a fault zone is significant for tunnel stability evaluation.In this study,a series of unconfined compression tests...Understanding the strength characteristics and deformation behaviour of the tunnel surrounding rock in a fault zone is significant for tunnel stability evaluation.In this study,a series of unconfined compression tests were conducted to investigate the mechanical characteristics and failure behaviour of completely weathered granite(CWG)from a fault zone,considering with height-diameter(h/d)ratio,dry densities(ρd)and moisture contents(ω).Based on the experimental results,a regression mathematical model of unconfined compressive strength(UCS)for CWG was developed using the Multiple Nonlinear Regression method(MNLR).The research results indicated that the UCS of the specimen with a h/d ratio of 0.6 decreased with the increase ofω.When the h/d ratio increased to 1.0,the UCS increasedωwith up to 10.5%and then decreased.Increasingρd is conducive to the improvement of the UCS at anyω.The deformation and rupture process as well as final failure modes of the specimen are controlled by h/d ratio,ρd andω,and the h/d ratio is the dominant factor affecting the final failure mode,followed byωandρd.The specimens with different h/d ratio exhibited completely different fracture mode,i.e.,typical splitting failure(h/d=0.6)and shear failure(h/d=1.0).By comparing the experimental results,this regression model for predicting UCS is accurate and reliable,and the h/d ratio is the dominant factor affecting the UCS of CWG,followed byρd and thenω.These findings provide important references for maintenance of the tunnel crossing other fault fractured zones,especially at low confining pressure or unconfined condition.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
In the present study, a statistical investigation is carried out to explore whether there is a relationship between the critical frequency （foEs） of the sporadic-E layer that is occasionally seen on the E region of ...In the present study, a statistical investigation is carried out to explore whether there is a relationship between the critical frequency （foEs） of the sporadic-E layer that is occasionally seen on the E region of the ionosphere and the quasi- biennial oscillation （QBO） that flows in the east-west direction in the equatorial stratosphere. Multiple regression model as a statistical tool was used to determine the relationship between variables. In this model, the stationarity of the variables （foEs and QBO） was firstly analyzed for each station （Cocos Island, Gibilmanna, Niue Island, and Tahiti）. Then, a co- integration test was made to determine the existence of a long-term relationship between QBO and foes. After verifying the presence of a long-term relationship between the variables, the magnitude of the relationship between variables was further determined using the multiple regression model. As a result, it is concluded that the variations in foEs were explainable with QBO measured at 10 hPa altitude at the rate of 69%, 94%, 79%, and 58% for Cocos Island, Gibilmanna, Niue Island, and Tahiti stations, respectively. It is observed that the variations in foes were explainable with QBO measured at 70 hPa altitude at the rate of 66%, 69%, 53%, and 47% for Cocos Island, Gibilmanna, Niue Island, and Tahiti stations, respectively.展开更多
Cyperus papyrus (L.) growth rate and mortality is influenced by environmental conditions prevailing in the wetland. To assess growth dynamics of C. papyrus in relation to water depth and anthropogenic (exploitation) p...Cyperus papyrus (L.) growth rate and mortality is influenced by environmental conditions prevailing in the wetland. To assess growth dynamics of C. papyrus in relation to water depth and anthropogenic (exploitation) pressures, monthly and bi-monthly measurement of culm length and girth were done between June and December 2010 (period 1) and April to June 2011 (period 2). Three study sites were selected based on the water levels and livelihood-driven exploitation pressures. Surrogate measurements of individual culm height and girth were done in three 1 m2 quadrats in each site to determine the growth rate of papyrus. Water depth was lowest in period 2 (dry) and highest in period 1 (wet) which was related to the livelihood activities being highest in period two and lowest in period one. Culm mortality occurred throughout the study period with 64% due to natural senescing while insect/rodent accounted for 19%. Papyrus growth was higher in Singida (2.5 ± 0.2 cm/day) representing less disturbed site and least in Wasare (1.4 ± 0.1 cm/day) which was highly disturbed. Multiple regression models for culm length showed culm density, mean length and NH4 negatively influenced growth rate while site as a dummy variable, water depth, SRP and TP had positive effects on papyrus growth rate. Understanding growth rate and causes of mortality in papyrus is important to establish sustainable management strategies of this ecosystem to maintain its integrity.展开更多
In this paper,meteorological industry standard,daily mean temperature,and an improved multiple regression model are used to calculate China's climatic seasons,not only to help understand their spatio-temporal dist...In this paper,meteorological industry standard,daily mean temperature,and an improved multiple regression model are used to calculate China's climatic seasons,not only to help understand their spatio-temporal distribution,but also to provide a reference for China's climatic regionalization and crop production.It is found that the improved multiple regression model can accurately show the spatial distribution of climatic seasons.The main results are as follows.There are four climatic seasonal regions in China,namely,the perennial-winter,no-winter,no-summer and discernible regions,and their ranges basically remained stable from 1951 to 2017.The cumulative anomaly curve of the four climatic seasonal regions clarifies that the trend of China's climatic seasonal regions turned in 1994,after which the area of the perennial-winter and no-summer regions narrowed and the no-winter and discernible regions expanded.The number of sites with significantly reduced winter duration is the largest,followed by the number of sites with increased summer duration,and the number of sites with large changes in spring and autumn is the least.Spring advances and autumn is postponed due to the shortened winter and lengthened summer durations.Sites with significant change in seasonal duration are mainly distributed in Northwest China,the Sichuan Basin,the Huanghe-Huaihe-Haihe(Huang-Huai-Hai) Plain,the Northeast China Plain,and the Southeast Coast.展开更多
基金funded by the National Natural Science Foundation of China(32072764, 31702121)the 2115 Talent Development Program of China Agricultural UniversityNational Key Research and Development Program of China (2019YFD1002605)
文摘Backgrounds:Evaluating the growth performance of pigs in real-time is laborious and expensive,thus mathematical models based on easily accessible variables are developed.Multiple regression(MR)is the most widely used tool to build prediction models in swine nutrition,while the artificial neural networks(ANN)model is reported to be more accurate than MR model in prediction performance.Therefore,the potential of ANN models in predicting the growth performance of pigs was evaluated and compared with MR models in this study.Results:Body weight(BW),net energy(NE)intake,standardized ileal digestible lysine(SID Lys)intake,and their quadratic terms were selected as input variables to predict ADG and F/G among 10 candidate variables.In the training phase,MR models showed high accuracy in both ADG and F/G prediction(R^(2)_(ADG)=0.929,R^(2)_(F/G)=0.886)while ANN models with 4,6 neurons and radial basis activation function yielded the best performance in ADG and F/G prediction(R^(2)_(ADG)=0.964,R^(2)_(F/G)=0.932).In the testing phase,these ANN models showed better accuracy in ADG prediction(CCC:0.976 vs.0.861,R^(2):0.951 vs.0.584),and F/G prediction(CCC:0.952 vs.0.900,R^(2):0.905 vs.0.821)compared with the MR models.Meanwhile,the“over-fitting”occurred in MR models but not in ANN models.On validation data from the animal trial,ANN models exhibited superiority over MR models in both ADG and F/G prediction(P<0.01).Moreover,the growth stages have a significant effect on the prediction accuracy of the models.Conclusion:Body weight,NE intake and SID Lys intake can be used as input variables to predict the growth performance of growing-finishing pigs,with trained ANN models are more flexible and accurate than MR models.Therefore,it is promising to use ANN models in related swine nutrition studies in the future.
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.
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During underground coal gasification （UCG）, whereby coal is converted to syngas in situ, a cavity is formed in the coal seam. The cavity growth rate （CGR） or the moving rate of the gasification face is affected by controllable （operation pressure, gasification time, geometry of UCG panel） and uncontrollable （coal seam properties） factors. The CGR is usually predicted by mathematical models and laboratory experiments, which are time consuming, cumbersome and expensive. In this paper, a new simple model for CGR is developed using non-linear regression analysis, based on data from 1 l UCG field trials. The empirical model compares satisfactorily with Perkins model and can reliably predict CGR.
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.
基金supported by the Major Science and Technology Project of Inner Mongolia Autonomous Region (2024JBGS0011-02)Foundation for Innovative Research Groups in Basic Research of Gansu Province (25JRRA490)+1 种基金Youth Innovation Promotion Association of the Chinese Academy of Sciences (2022437)National Natural Science Foundation of China (42207538)。
文摘Amid global climate change, rising levels of nitrogen(N) deposition have attracted considerable attention for their potential effects on the carbon cycle of terrestrial ecosystems. The desert steppes are a crucial yet vulnerable ecosystem in arid areas, but their response to the combination of N addition and precipitation(a crucial factor in arid areas) remains underexplored. This study systematically explored the impact of N addition and precipitation on net ecosystem exchange(NEE) in a desert steppe in northern China. Specifically, we conducted a 2-a experiment from 2022 to 2023 with eight N addition treatments in the Urat desert steppe of Inner Mongolia Autonomous Region, China, to examine changes in NEE and explore its driving factors. The structural equation model(SEM) and multiple regression model were applied to determine the relationship of NEE with plant community characteristics and soil physical-chemical properties. Statistical results showed that N addition has no significant effect on NEE.However, it has a significant impact on the functional traits of desert steppe plant communities. SEM results further revealed that N addition has no significant effect on NEE in the desert steppe, whereas annual precipitation can influence NEE variations. The multiple regression model analysis indicated that plant functional traits play an important role in explaining the changes in NEE, accounting for 62.15% of the variation in NEE. In addition, plant height, as an important plant functional trait indicator, shows stronger reliability in predicting the changes in NEE and becomes a more promising predictor. These findings provide valuable insights into the complex ecological mechanisms governing plant community responses to precipitation and nutrient availability in the arid desert steppes, contributing to the improved monitoring and prediction of desert steppe ecosystem responses to global climate change.
文摘The study on the factors affecting the performance of small and medium enterprises(SMEs)in Lao PDR aims at firstly investigating the general characteristics of SMEs in Lao PDR,and secondly defining the factors affecting business performance of small and medium enterprises in Lao PDR.The secondary data(2018)surveyed by World Bank enterprises were employed.The descriptive statistics were employed in order to see whether independent and dependent variables have impacts on SMEs’business performance.It was found that all small and medium enterprises were from family-based enterprises with no competition and no application of science and technology.It also revealed that the experiences of managers,services,duration of business implementation,number of laborers,number of trainings,export,access to finance,and innovation have significant relationship with SMEs’business performance.Whereas,it was found that gender of managers,size of SMEs,location,type of enterprises:manufacturing and retail,have no significant relationship with SMEs’business performance in Lao PDR.
基金financial support from the Shanxi Scholarship Council of China(No.2024-057)the Water Conservancy Science and Technology Research and Promotion Project of Shanxi Province,China(No.2025GM13)the National Natural Science Foundation of China(No.52571048).
文摘The heteroepitaxy of diamond films has received widespread attention;however,its application remains limited owing to the mismatch in properties and structure between diamond and heterogeneous substrates.In this study,diamond films were successfully synthesized on high-entropy alloys(HEAs)substrates using microwave plasma chemical vapor deposition.The resulting diamond films were continuous,uniform,and adhered to the HEAs substrates.The mixed carbides were identified using X-ray diffraction,and the quality of the diamond films was examined using Raman spectroscopy.Moreover,the corrosion test revealed that the diamond/TiZrHfMo samples had excellent electrochemical stability and corrosion resistance with a corrosion potential value of-0.169 V in a 3.5wt%NaCl solution.A multiple regression model was established to evaluate the effects of the structure and growth parameters,which confirmed that the mixing entropy significantly affected the grain size and corrosion properties.
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.
文摘An accurate assessment of the property value is very important to make a deal, property tax, and mortgage for loan. The mass appraisal system has been developed in some foreign countries, especially in American for a long time. In Taiwan, we still have few experiences in using computer-assisted mass appraisal system, especially using artificial neural network （ANN）. This article has two objectives： （1） to illustrate application of ANN to the Kaohsiung property market by the method of back-propagation. The study is based on the properties data of sales price, we also use multiple regressions in the same data; （2） to evaluate the performance of two models by using the mean absolute percentage error （MAPE） and hit ratio （HR）. This paper finds that using artificial neural network （ANN） is able to overcome multiple regressions＇ methodological problems and also get better performance than multiple regression model （MRA）. These results are useful in helping local government to assess their assessment value.
文摘This study developed empirical-mathematical models to predict the California Bearing Ratio (CBR) using soil index properties in Ogbia-Nembe road in the Niger Delta region of Nigeria. The determination of CBR of soil is a laborious operation that requires a longer time and materials leading to increased cost and schedule;this can be reduced by adopting an empirical-mathematical model that can predict the CBR using other simpler soil index properties such as Plastic Limit (PL), the Liquid Limit (LL), the Plasticity Index (PI) and the Moisture Content (MC), which are less laborious and take lesser time to obtain. Thirteen models were developed to understand the relationship between these soil index properties: the independent variable and the California Bearing Ratio (CBR): the dependent variable;Six linear, Six quadratic and One multiple linear regression models were developed for this relationship. Analysis of variance (ANOVA) on the thirteen models showed that the Optimum Moisture Content (OMC) and the Maximum Dry Density (MDD) are better independent variables for the prediction of the CBR value of Ogbia-Nembe soil generating a quadratic model and a multiple linear regression model having a better coefficient of determination R<sup>2</sup> = 0.96 and 0.94 respectively, mean square error (MSE) of 0.74 and 1.152 respectively with Root mean square errors of 0.861 and 1.073 accordingly. These models were used to predict the CBR of the soil. The CBR values predicted by the model were further compared with those of the actual experimental test and found to be relatively consistent with minimal variance. This establishes that CBR of any soil can be predicted from the Index Property of the soil and this is more economical and takes lesser time and can be universally adopted for soil investigation.
文摘The aim of this study was to model the Undrained Shear Strength (USS) of soil found in the coastal region of the Niger Delta in Nigeria with some soil properties. The undrained shear strength (USS) is a key parameter needed for most geotechnical/structural designs. Accurate determination of the USS of soft clays can be challenging to obtain in the laboratory due to the difficulty in remoulding the clay to its in-situ conditions before testing and more accurate test such as Cone Penetration test (CPT) can be quite expensive. This study was carried out at Escravos site which is located in Delta state, Nigeria. Three Boreholes were drilled and soil samples were collected at 0.75 m intervals up to a depth of 45 m. Laboratory tests were used to obtain the moisture content, bulk unit weight, liquid and plastic limit, while CPT was used in obtaining the undrained shear strength. Classification of the soil samples was done by adopting the Unified Soil Classification System and various models relating the USS with the soil properties were developed. The result showed that most of the soils at Escravos site were predominately inorganic clay of high plasticity which are problematic due to the expansion and shrinking nature of this type of soil. The model developed showed that the soil properties that gave the best fit with the USS were the moisture content and effective stress of the soil. The coefficient of determination (R<sup>2</sup>) and the root mean square error (RMSE) obtained for this model were 0.805 and 6.37 KN/m<sup>2</sup>, respectively.
基金supported by the National Natural Science Foundation of China,NSFC(No.42202318).
文摘Understanding the strength characteristics and deformation behaviour of the tunnel surrounding rock in a fault zone is significant for tunnel stability evaluation.In this study,a series of unconfined compression tests were conducted to investigate the mechanical characteristics and failure behaviour of completely weathered granite(CWG)from a fault zone,considering with height-diameter(h/d)ratio,dry densities(ρd)and moisture contents(ω).Based on the experimental results,a regression mathematical model of unconfined compressive strength(UCS)for CWG was developed using the Multiple Nonlinear Regression method(MNLR).The research results indicated that the UCS of the specimen with a h/d ratio of 0.6 decreased with the increase ofω.When the h/d ratio increased to 1.0,the UCS increasedωwith up to 10.5%and then decreased.Increasingρd is conducive to the improvement of the UCS at anyω.The deformation and rupture process as well as final failure modes of the specimen are controlled by h/d ratio,ρd andω,and the h/d ratio is the dominant factor affecting the final failure mode,followed byωandρd.The specimens with different h/d ratio exhibited completely different fracture mode,i.e.,typical splitting failure(h/d=0.6)and shear failure(h/d=1.0).By comparing the experimental results,this regression model for predicting UCS is accurate and reliable,and the h/d ratio is the dominant factor affecting the UCS of CWG,followed byρd and thenω.These findings provide important references for maintenance of the tunnel crossing other fault fractured zones,especially at low confining pressure or unconfined condition.
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.
文摘In the present study, a statistical investigation is carried out to explore whether there is a relationship between the critical frequency （foEs） of the sporadic-E layer that is occasionally seen on the E region of the ionosphere and the quasi- biennial oscillation （QBO） that flows in the east-west direction in the equatorial stratosphere. Multiple regression model as a statistical tool was used to determine the relationship between variables. In this model, the stationarity of the variables （foEs and QBO） was firstly analyzed for each station （Cocos Island, Gibilmanna, Niue Island, and Tahiti）. Then, a co- integration test was made to determine the existence of a long-term relationship between QBO and foes. After verifying the presence of a long-term relationship between the variables, the magnitude of the relationship between variables was further determined using the multiple regression model. As a result, it is concluded that the variations in foEs were explainable with QBO measured at 10 hPa altitude at the rate of 69%, 94%, 79%, and 58% for Cocos Island, Gibilmanna, Niue Island, and Tahiti stations, respectively. It is observed that the variations in foes were explainable with QBO measured at 70 hPa altitude at the rate of 66%, 69%, 53%, and 47% for Cocos Island, Gibilmanna, Niue Island, and Tahiti stations, respectively.
文摘Cyperus papyrus (L.) growth rate and mortality is influenced by environmental conditions prevailing in the wetland. To assess growth dynamics of C. papyrus in relation to water depth and anthropogenic (exploitation) pressures, monthly and bi-monthly measurement of culm length and girth were done between June and December 2010 (period 1) and April to June 2011 (period 2). Three study sites were selected based on the water levels and livelihood-driven exploitation pressures. Surrogate measurements of individual culm height and girth were done in three 1 m2 quadrats in each site to determine the growth rate of papyrus. Water depth was lowest in period 2 (dry) and highest in period 1 (wet) which was related to the livelihood activities being highest in period two and lowest in period one. Culm mortality occurred throughout the study period with 64% due to natural senescing while insect/rodent accounted for 19%. Papyrus growth was higher in Singida (2.5 ± 0.2 cm/day) representing less disturbed site and least in Wasare (1.4 ± 0.1 cm/day) which was highly disturbed. Multiple regression models for culm length showed culm density, mean length and NH4 negatively influenced growth rate while site as a dummy variable, water depth, SRP and TP had positive effects on papyrus growth rate. Understanding growth rate and causes of mortality in papyrus is important to establish sustainable management strategies of this ecosystem to maintain its integrity.
基金National Natural Science Foundation of China,No.41561024,No.31760241,No.41801054。
文摘In this paper,meteorological industry standard,daily mean temperature,and an improved multiple regression model are used to calculate China's climatic seasons,not only to help understand their spatio-temporal distribution,but also to provide a reference for China's climatic regionalization and crop production.It is found that the improved multiple regression model can accurately show the spatial distribution of climatic seasons.The main results are as follows.There are four climatic seasonal regions in China,namely,the perennial-winter,no-winter,no-summer and discernible regions,and their ranges basically remained stable from 1951 to 2017.The cumulative anomaly curve of the four climatic seasonal regions clarifies that the trend of China's climatic seasonal regions turned in 1994,after which the area of the perennial-winter and no-summer regions narrowed and the no-winter and discernible regions expanded.The number of sites with significantly reduced winter duration is the largest,followed by the number of sites with increased summer duration,and the number of sites with large changes in spring and autumn is the least.Spring advances and autumn is postponed due to the shortened winter and lengthened summer durations.Sites with significant change in seasonal duration are mainly distributed in Northwest China,the Sichuan Basin,the Huanghe-Huaihe-Haihe(Huang-Huai-Hai) Plain,the Northeast China Plain,and the Southeast Coast.
