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Country-based modelling of COVID-19 case fatality rate:A multiple regression analysis
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作者 Soodeh Sagheb Ali Gholamrezanezhad +2 位作者 Elizabeth Pavlovic Mohsen Karami Mina Fakhrzadegan 《World Journal of Virology》 2024年第1期84-94,共11页
BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale c... BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale cannot be fully understood due to lack of information.AIM To identify key factors that may explain the variability in case lethality across countries.METHODS We identified 21 Potential risk factors for coronavirus disease 2019(COVID-19)case fatality rate for all the countries with available data.We examined univariate relationships of each variable with case fatality rate(CFR),and all independent variables to identify candidate variables for our final multiple model.Multiple regression analysis technique was used to assess the strength of relationship.RESULTS The mean of COVID-19 mortality was 1.52±1.72%.There was a statistically significant inverse correlation between health expenditure,and number of computed tomography scanners per 1 million with CFR,and significant direct correlation was found between literacy,and air pollution with CFR.This final model can predict approximately 97%of the changes in CFR.CONCLUSION The current study recommends some new predictors explaining affect mortality rate.Thus,it could help decision-makers develop health policies to fight COVID-19. 展开更多
关键词 COVID-19 SARS-CoV-2 Case fatality rate Predictive model multiple regression
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Enterprise Value Valuation-BYD as an Example Based on SWOT Model and Multiple Regression Model
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作者 YANG Siqi 《Management Studies》 2024年第4期197-217,共21页
BYD is one of the largest new energy vehicle companies in China.Analyzing its scenario and the factors that affect its value helps to understand and identify development opportunities and potential problems.On one han... BYD is one of the largest new energy vehicle companies in China.Analyzing its scenario and the factors that affect its value helps to understand and identify development opportunities and potential problems.On one hand,this paper makes a qualitative analysis of BYD,using SWOT model to study the internal capability and external environment of BYD.On the other hand,the multiple regression model is used for quantitative analysis of BYD’s enterprise value,and the model is established based on three factors:enterprise fundamentals,investor behavior and psychology,and macroeconomic policy uncertainty,and the stepwise regression is carried out.The results show that the increase of institutional investors’shareholding ratio,the increase of investor sentiment index,and the increase of M2 growth rate will increase the overall enterprise value,while the increase of economic policy uncertainty will decrease the enterprise value. 展开更多
关键词 BYD enterprise value multiple regression analysis investor behavior and psychology macroeconomic policy uncertainty
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Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas:A Case Study of Songnen Plain,Northeast China
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作者 ZHANG Sumei ZHANG Yuan ZHAO Hongmei 《Chinese Geographical Science》 SCIE CSCD 2024年第3期548-563,共16页
The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate ... The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data. 展开更多
关键词 crop residue burning burned area Sentinel-2 Multi Spectral Instrument(MSI) logistic regression Songnen Plain China
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Infiltration by monocytes of the central nervous system and its role in multiple sclerosis: reflections on therapeutic strategies
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作者 Guangyong Zhang Qing Yao +9 位作者 Chubing Long Pengcheng Yi Jiali Song Luojia Wu Wei Wan Xiuqin Rao Yue Lin Gen Wei Jun Ying Fuzhou Hua 《Neural Regeneration Research》 SCIE CAS 2025年第3期779-793,共15页
Mononuclear macrophage infiltration in the central nervous system is a prominent feature of neuroinflammation. Recent studies on the pathogenesis and progression of multiple sclerosis have highlighted the multiple rol... Mononuclear macrophage infiltration in the central nervous system is a prominent feature of neuroinflammation. Recent studies on the pathogenesis and progression of multiple sclerosis have highlighted the multiple roles of mononuclear macrophages in the neuroinflammatory process. Monocytes play a significant role in neuroinflammation, and managing neuroinflammation by manipulating peripheral monocytes stands out as an effective strategy for the treatment of multiple sclerosis, leading to improved patient outcomes. This review outlines the steps involved in the entry of myeloid monocytes into the central nervous system that are targets for effective intervention: the activation of bone marrow hematopoiesis, migration of monocytes in the blood, and penetration of the blood–brain barrier by monocytes. Finally, we summarize the different monocyte subpopulations and their effects on the central nervous system based on phenotypic differences. As activated microglia resemble monocyte-derived macrophages, it is important to accurately identify the role of monocyte-derived macrophages in disease. Depending on the roles played by monocyte-derived macrophages at different stages of the disease, several of these processes can be interrupted to limit neuroinflammation and improve patient prognosis. Here, we discuss possible strategies to target monocytes in neurological diseases, focusing on three key aspects of monocyte infiltration into the central nervous system, to provide new ideas for the treatment of neurodegenerative diseases. 展开更多
关键词 blood–brain barrier MACROPHAGES MONOCYTES multiple sclerosis NEUROINFLAMMATION review therapy
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Constitutive equations of 1060 pure aluminum based on modified double multiple nonlinear regression model 被引量:6
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作者 李攀 李付国 +2 位作者 曹俊 马新凯 李景辉 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2016年第4期1079-1095,共17页
In order to study the work-ability and establish the optimum hot formation processing parameters for industrial 1060 pure aluminum, the compressive deformation behavior of pure aluminum was investigated at temperature... In order to study the work-ability and establish the optimum hot formation processing parameters for industrial 1060 pure aluminum, the compressive deformation behavior of pure aluminum was investigated at temperatures of 523?823 K and strain rates of 0.005?10 s?1 on a Gleeble?1500 thermo-simulation machine. The influence rule of processing parameters (strain, strain rate and temperature) on flow stress of pure aluminum was investigated. Nine analysis factors consisting of material parameters and according weights were optimized. Then, the constitutive equations of multilevel series rules, multilevel parallel rules and multilevel series &parallel rules were established. The correlation coefficients (R) are 0.992, 0.988 and 0.990, respectively, and the average absolute relative errors (AAREs) are 6.77%, 8.70% and 7.63%, respectively, which proves that the constitutive equations of multilevel series rules can predict the flow stress of pure aluminum with good correlation and precision. 展开更多
关键词 1060 pure aluminum modified DMNR(double multiple nonlinear regression) constitutive equation flow behaviour multilevel series rules multilevel parallel rules multilevel series & parallel rules
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Stepwise multiple regressions application in liposome orthogonal experiments
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作者 范晓婧 刘倩 +2 位作者 甄鹏 张扬 胡新 《Journal of Chinese Pharmaceutical Sciences》 CAS 2007年第2期96-100,共5页
Aim New statistical method was applied in data analysis of orthogonal experiments to optimize the preparation of liposome. Method Particle size, zeta potential, encapsulation efficiency and physical stability of lipos... Aim New statistical method was applied in data analysis of orthogonal experiments to optimize the preparation of liposome. Method Particle size, zeta potential, encapsulation efficiency and physical stability of liposomes were selected by orthogonal design as evaluating indicators. Through three statistical methods (direct observation, variance analysis and stepwise multiple regression), the optimized preparing conditions were acquired and validated by experiment. Results All of the four indicators were different by these analyses. The validation experiments indicated that the optimized conditions by stepwise multiple regressions were better than that by traditional analysis. Conclusion Experiment results suggested that multiple regressions could avoid the weakness of direct observation and variance analysis, but more work should be done in preparing liposomes. 展开更多
关键词 Orthogonal experiment LIPOSOME Stepwise multiple regressions
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Combined model based on optimized multi-variable grey model and multiple linear regression 被引量:11
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作者 Pingping Xiong Yaoguo Dang +1 位作者 Xianghua wu Xuemei Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2011年第4期615-620,共6页
The construction method of background value is improved in the original multi-variable grey model (MGM(1,m)) from its source of construction errors. The MGM(1,m) with optimized background value is used to elimin... The construction method of background value is improved in the original multi-variable grey model (MGM(1,m)) from its source of construction errors. The MGM(1,m) with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM(1,2) with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction. 展开更多
关键词 multi-variable grey model (MGM(1 m)) backgroundvalue OPTIMIZATION multiple linear regression combined predic-tion model.
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Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network(ANN) and multiple linear regressions(MLR) 被引量:8
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作者 Ali Mohammadi Torkashvand Abbas Ahmadi Niloofar Layegh Nikravesh 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2017年第7期1634-1644,共11页
Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit, in recent decades, artificial intelligence s... Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit, in recent decades, artificial intelligence systems were employed for developing predictive models to estimate and predict many agriculture processes. In the present study, the predictive capabilities of multiple linear regressions (MLR) and artificial neural networks (ANNs) are evaluated to estimate fruit firmness in six months, including each of nutrients concentrations (nitrogen (N), potassium (K), calcium (Ca) and magnesium (Mg)) alone (P1), com- bination of nutrients concentrations (P2), nutrient concentration ratios alone (P3), and combination of nutrient concentrations and nutrient concentration ratios (P4). The results showed that MLR model estimated fruit firmness more accuracy than ANN model in three datasets (P1, P2 and P4). However, the application of P3 (N/Ca ratio) as the input dataset in ANN model improved the prediction of fruit firmness than the MLR model. Correlation coefficient and root mean squared error (RMSE) were 0.850 and 0.539 between the measured and the estimated data by the ANN model, respectively. Generally, the ANN model showed greater potential in determining the relationship between 6-mon-fruit firmness and nutrients concentration. 展开更多
关键词 artificial neural network FIRMNESS FRUIT KIWI multiple linear regression NUTRIENT
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Predicting the growth performance of growing-finishing pigs based on net energy and digestible lysine intake using multiple regression and artificial neural networks models 被引量:8
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作者 Li Wang Qile Hu +3 位作者 Lu Wang Huangwei Shi Changhua Lai Shuai Zhang 《Journal of Animal Science and Biotechnology》 SCIE CAS CSCD 2022年第6期1932-1944,共13页
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
关键词 multiple regression model Neural networks PIG PREDICTION
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A study of the mixed layer of the South China Sea based on the multiple linear regression 被引量:8
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作者 DUAN Rui YANG Kunde +1 位作者 MA Yuanliang HU Tao 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2012年第6期19-31,共13页
Multiple linear regression (MLR) method was applied to quantify the effects of the net heat flux (NHF), the net freshwater flux (NFF) and the wind stress on the mixed layer depth (MLD) of the South China Sea ... Multiple linear regression (MLR) method was applied to quantify the effects of the net heat flux (NHF), the net freshwater flux (NFF) and the wind stress on the mixed layer depth (MLD) of the South China Sea (SCS) based on the simple ocean data assimilation (SODA) dataset. The spatio-temporal distributions of the MLD, the buoyancy flux (combining the NHF and the NFF) and the wind stress of the SCS were presented. Then using an oceanic vertical mixing model, the MLD after a certain time under the same initial conditions but various pairs of boundary conditions (the three factors) was simulated. Applying the MLR method to the results, regression equations which modeling the relationship between the simulated MLD and the three factors were calculated. The equations indicate that when the NHF was negative, it was the primary driver of the mixed layer deepening; and when the NHF was positive, the wind stress played a more important role than that of the NHF while the NFF had the least effect. When the NHF was positive, the relative quantitative effects of the wind stress, the NHF, and the NFF were about i0, 6 and 2. The above conclusions were applied to explaining the spatio-temporal distributions of the MLD in the SCS and thus proved to be valid. 展开更多
关键词 mixed layer multiple linear regression South China Sea vertical mixing model
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Stability of mine ventilation system based on multiple regression analysis 被引量:12
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作者 JIA Ting-gui LIU Jian 《Mining Science and Technology》 EI CAS 2009年第4期463-466,共4页
In order to overcome the disadvantages of diagonal connection structures that are complex and for which it is difficult to derive the discriminant of the airflow directions of airways, we have applied a multiple regre... In order to overcome the disadvantages of diagonal connection structures that are complex and for which it is difficult to derive the discriminant of the airflow directions of airways, we have applied a multiple regression method to analyze the effect, of changing the rules of mine airflows, on the stability of a mine ventilation system. The amount of air ( Qj ) is determined for the major airway and an optimum regression equation was derived for Qi as a function of the independent variable ( Ri ), i.e., the venti- lation resistance between different airways. Therefore, corresponding countermeasures are proposed according to the changes in airflows. The calculated results agree very well with our practical situation, indicating that multiple regression analysis is simple, quick and practical and is therefore an effective method to analyze the stability of mine ventilation systems. 展开更多
关键词 ventilation network STABILITY diagonal connection multiple regression analysis
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Statistical analysis of nitrogen use efficiency in Northeast China using multiple linear regression and Random Forest 被引量:2
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作者 LIU Ying-xia Gerard B.M.HEUVELINK +4 位作者 Zhanguo BAI HE Ping JIANG Rong HUANG Shaohui XU Xin-peng 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2022年第12期3637-3657,共21页
Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the applica... Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-researched.In this study,stepwise multiple linear regression(SMLR)and Random Forest(RF)were used to evaluate the spatial and temporal variation of NUE indicators(i.e.,partial factor productivity of N(PFPN);partial nutrient balance of N(PNBN))at county scale in Northeast China(Heilongjiang,Liaoning and Jilin provinces)from 1990 to 2015.Explanatory variables included agricultural management practices,topography,climate,economy,soil and crop types.Results revealed that the PFPN was higher in the northern parts and lower in the center of the Northeast China and PNBN increased from southern to northern parts during the 1990–2015 period.The NUE indicators decreased with time in most counties during the study period.The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFPN,and 0.67 and 0.89 for PNBN,respectively.The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model.The planting area index of vegetables and beans,soil clay content,saturated water content,enhanced vegetation index in November&December,soil bulk density,and annual minimum temperature were the main explanatory variables for both NUE indicators.This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR.This novel study gives reference measurements to improve crop NUE which is one of the most effective means of managing N for sustainable development,ensuring food security,alleviating environmental degradation and increasing farmer’s profitability. 展开更多
关键词 partial factor productivity of N partial nutrient balance of N stepwise multiple linear regression Random Forest county scale Northeast China
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Prediction of cavity growth rate during underground coal gasification using multiple regression analysis 被引量:8
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作者 Mehdi Najafi Seyed Mohammad Esmaiel Jalali +1 位作者 Reza KhaloKakaie Farrokh Forouhandeh 《International Journal of Coal Science & Technology》 EI 2015年第4期318-324,共7页
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
关键词 Underground coal gasification (UCG) - Cavity growth rate . multiple regression analysis ~ Empirical model
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New empirical model to evaluate groundwater flow into circular tunnel using multiple regression analysis 被引量:6
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作者 Farhadian Hadi Katibeh Homayoon 《International Journal of Mining Science and Technology》 SCIE EI CSCD 2017年第3期415-421,共7页
There are various analytical, empirical and numerical methods to calculate groundwater inflow into tun- nels excavated in rocky media. Analytical methods have been widely applied in prediction of groundwa- ter inflow ... There are various analytical, empirical and numerical methods to calculate groundwater inflow into tun- nels excavated in rocky media. Analytical methods have been widely applied in prediction of groundwa- ter inflow to tunnels due to their simplicity and practical base theory. Investigations show that the real amount of water infiltrating into jointed tunnels is much less than calculated amount using analytical methods and obtained results are very dependent on tunnel's geometry and environmental situations. In this study, using multiple regression analysis, a new empirical model for estimation of groundwater seepage into circular tunnels was introduced. Our data was acquired from field surveys and laboratory analysis of core samples. New regression variables were defined after perusing single and two variables relationship between groundwater seepage and other variables. Finally, an appropriate model for estima- tion of leakage was obtained using the stepwise algorithm. Statistics like R, R2, R2e and the histogram of residual values in the model represent a good reputation and fitness for this model to estimate the groundwater seepage into tunnels. The new experimental model was used for the test data and results were satisfactory. Therefore, multiple regression analysis is an effective and efficient way to estimate the groundwater seeoage into tunnels. 展开更多
关键词 Groundwater inflow Analytical equation multiple regression analysis Stepwise algorithm Tunnel
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Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks 被引量:5
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作者 Ghiasi Majid Askarnejad Nematollah +1 位作者 Dindarloo Saeid R. Shamsoddini Hamed 《International Journal of Mining Science and Technology》 SCIE EI CSCD 2016年第2期183-184,共2页
The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boul... The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine,Iran was predicted via multiple regression method and artificial neural networks.Results of 33 blasts in the mine were collected for modeling.Input variables were:joints spacing,density and uniaxial compressive strength of the intact rock,burden,spacing,stemming,bench height to burden ratio,and specific charge.The dependent variable was ratio of boulder volume to pattern volume.Both techniques were successful in predicting the ratio.In this study,the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19,respectively. 展开更多
关键词 Blast boulder Artificial neural networks multiple regression Golegohar iron ore mine
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Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming 被引量:1
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作者 Bijan Afrasiabian Mosleh Eftekhari 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2022年第5期1421-1432,共12页
Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to p... Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and elastic modulus(E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets.Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of KIC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination(R2),root mean square error(RMSE), and mean absolute error(MAE) were utilized herein. In the case of testing datasets, the values of R2, RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156,respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R2value and lower errors. 展开更多
关键词 Mode I fracture Toughness Critical stress intensity factor Linear multiple regression(LMR) Gene expression programming(GEP)
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Multiple regression analysis of risk factors related to radiation pneumonitis 被引量:1
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作者 Ling-Ling Shi Jiang-Hua Yang Hong-Fa Yao 《World Journal of Clinical Cases》 SCIE 2023年第5期1040-1048,共9页
BACKGROUND Radiation pneumonitis(RP)is a severe complication of thoracic radiotherapy that may lead to dyspnea and lung fibrosis,and negatively affects patients’quality of life.AIM To carry out multiple regression an... BACKGROUND Radiation pneumonitis(RP)is a severe complication of thoracic radiotherapy that may lead to dyspnea and lung fibrosis,and negatively affects patients’quality of life.AIM To carry out multiple regression analysis on the influencing factors of radiation pneumonitis.METHODS Records of 234 patients receiving chest radiotherapy in Huzhou Central Hospital(Huzhou,Zhejiang Province,China)from January 2018 to February 2021,and the patients were divided into either a study group or a control group based on the presence of radiation pneumonitis or not.Among them,93 patients with radiation pneumonitis were included in the study group and 141 without radiation pneumonitis were included in the control group.General characteristics,and radiation and imaging examination data of the two groups were collected and compared.Due to the statistical significance observed,multiple regression analysis was performed on age,tumor type,chemotherapy history,forced vital capacity(FVC),forced expiratory volume in the first second(FEV1),carbon monoxide diffusion volume(DLCO),FEV1/FVC ratio,planned target area(PTV),mean lung dose(MLD),total number of radiation fields,percentage of lung tissue in total lung volume(vdose),probability of normal tissue complications(NTCP),and other factors.RESULTS The proportions of patients aged≥60 years and those with the diagnosis of lung cancer and a history of chemotherapy in the study group were higher than those in the control group(P<0.05);FEV1,DLCO,and FEV1/FVC ratio in the study group were lower than those in the control group(P<0.05),while PTV,MLD,total field number,vdose,and NTCP were higher than in the control group(P<0.05).Logistic regression analysis showed that age,lung cancer diagnosis,chemotherapy history,FEV1,FEV1/FVC ratio,PTV,MLD,total number of radiation fields,vdose,and NTCP were risk factors for radiation pneumonitis.CONCLUSION We have identified patient age,type of lung cancer,history of chemotherapy,lung function,and radiotherapy parameters as risk factors for radiation pneumonitis.Comprehensive evaluation and examination should be carried out before radiotherapy to effectively prevent radiation pneumonitis. 展开更多
关键词 Radiation pneumonitis Influencing factors RADIOTHERAPY multiple regression analysis
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Optimization of rheological parameter for micro-bubble drilling fluids by multiple regression experimental design 被引量:2
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作者 郑力会 王金凤 +2 位作者 李潇鹏 张燕 李都 《Journal of Central South University》 SCIE EI CAS 2008年第S1期424-428,共5页
In order to optimize plastic viscosity of 18 mPa·s circulating micro-bubble drilling fluid formula,orthogonal and uniform experimental design methods were applied,and the plastic viscosities of 36 and 24 groups o... In order to optimize plastic viscosity of 18 mPa·s circulating micro-bubble drilling fluid formula,orthogonal and uniform experimental design methods were applied,and the plastic viscosities of 36 and 24 groups of agent were tested,respectively.It is found that these two experimental design methods show drawbacks,that is,the amount of agent is difficult to determine,and the results are not fully optimized.Therefore,multiple regression experimental method was used to design experimental formula.By randomly selecting arbitrary agent with the amount within the recommended range,17 groups of drilling fluid formula were designed,and the plastic viscosity of each experiment formula was measured.Set plastic viscosity as the objective function,through multiple regressions,then quadratic regression model is obtained,whose correlation coefficient meets the requirement.Set target values of plastic viscosity to be 18,20 and 22 mPa·s,respectively,with the trial method,5 drilling fluid formulas are obtained with accuracy of 0.000 3,0.000 1 and 0.000 3.Arbitrarily select target value of each of the two groups under the formula for experimental verification of drilling fluid,then the measurement errors between theoretical and tested plastic viscosity are less than 5%,confirming that regression model can be applied to optimizing the circulating of plastic-foam drilling fluid viscosity.In accordance with the precision of different formulations of drilling fluid for other constraints,the methods result in the optimization of the circulating micro-bubble drilling fluid parameters. 展开更多
关键词 orthogonal EXPERIMENTAL DESIGN uniform EXPERIMENTAL DESIGN CIRCULATING micro-bubbles plastic viscosity multiple regression EXPERIMENTAL DESIGN
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Prediction of Shear Wave Velocity Using Artificial Neural Network Technique, Multiple Regression and Petrophysical Data: A Case Study in Asmari Reservoir (SW Iran) 被引量:5
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作者 Habib Akhundi Mohammad Ghafoori Gholam-Reza Lashkaripour 《Open Journal of Geology》 2014年第7期303-313,共11页
Shear wave velocity has numerous applications in geomechanical, petrophysical and geophysical studies of hydrocarbon reserves. However, data related to shear wave velocity isn’t available for all wells, especially ol... Shear wave velocity has numerous applications in geomechanical, petrophysical and geophysical studies of hydrocarbon reserves. However, data related to shear wave velocity isn’t available for all wells, especially old wells and it is very important to estimate this parameter using other well logging. Hence, lots of methods have been developed to estimate these data using other available information of reservoir. In this study, after processing and removing inappropriate petrophysical data, we estimated petrophysical properties affecting shear wave velocity of the reservoir and statistical methods were used to establish relationship between effective petrophysical properties and shear wave velocity. To predict (VS), first we used empirical relationships and then multivariate regression methods and neural networks were used. Multiple regression method is a powerful method that uses correlation between available information and desired parameter. Using this method, we can identify parameters affecting estimation of shear wave velocity. Neural networks can also be trained quickly and present a stable model for predicting shear wave velocity. For this reason, this method is known as “dynamic regression” compared with multiple regression. Neural network used in this study is not like a black box because we have used the results of multiple regression that can easily modify prediction of shear wave velocity through appropriate combination of data. The same information that was intended for multiple regression was used as input in neural networks, and shear wave velocity was obtained using compressional wave velocity and well logging data (neutron, density, gamma and deep resistivity) in carbonate rocks. The results show that methods applied in this carbonate reservoir was successful, so that shear wave velocity was predicted with about 92 and 95 percents of correlation coefficient in multiple regression and neural network method, respectively. Therefore, we propose using these methods to estimate shear wave velocity in wells without this parameter. 展开更多
关键词 SHEAR Wave VELOCITY Petrophysical LOGS Neural Networks multiple regression Asmari RESERVOIR
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Research on the Effect of Artificial Intelligence Real Estate Forecasting Using Multiple Regression Analysis and Artificial Neural Network: A Case Study of Ghana 被引量:2
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作者 Madami Michael Ishaku Hill Isaac Lewu 《Journal of Computer and Communications》 2021年第10期1-14,共14页
To transition from conventional to intelligent real estate, the real estate industry must enhance its embrace of disruptive technology. Even though the real estate auction market has grown in importance in the financi... To transition from conventional to intelligent real estate, the real estate industry must enhance its embrace of disruptive technology. Even though the real estate auction market has grown in importance in the financial, economic, and investment sectors, few artificial intelligence-based research has tried to predict the auction values of real estate in the past. According to the objectives of this research, artificial intelligence and statistical methods will be used to create forecasting models for real estate auction prices. A multiple regression model and an artificial neural network are used in conjunction with one another to build the forecasting models. For the empirical study, the study utilizes data from Ghana apartment auctions from 2016 to 2020 to anticipate auction prices and evaluate the forecasting accuracy of the various models available at the time. Compared to the conventional Multiple Regression Analysis, using artificial intelligence systems for real estate appraisal is becoming a more viable option (MRA). The Artificial Neural network model exhibits the most outstanding performance, and efficient zonal segmentation based on the auction evaluation price enhances the model’s prediction accuracy even more. There is a statistically significant difference between the two models when it comes to forecasting the values of real estate auctions. 展开更多
关键词 Real Estate Forecasting Artificial Intelligence Artificial Neural Networks multiple regression Analysis
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