Power converters are essential components in modern life,being widely used in industry,automation,transportation,and household appliances.In many critical applications,their failure can lead not only to financial loss...Power converters are essential components in modern life,being widely used in industry,automation,transportation,and household appliances.In many critical applications,their failure can lead not only to financial losses due to operational downtime but also to serious risks to human safety.The capacitors forming the output filter,typically aluminumelectrolytic capacitors(AECs),are among the most critical and susceptible components in power converters.The electrolyte in AECs often evaporates over time,causing the internal resistance to rise and the capacitance to drop,ultimately leading to component failure.Detecting this fault requires measuring the current in the capacitor,rendering the method invasive and frequently impractical due to spatial constraints or operational limitations imposed by the integration of a current sensor in the capacitor branch.This article proposes the implementation of an online noninvasive fault diagnosis technique for estimating the Equivalent Series Resistance(ESR)and Capacitance(C)values of the capacitor,employing a combination of signal processing techniques(SPT)and machine learning(ML)algorithms.This solution relies solely on the converter’s input and output signals,therefore making it a non-invasive approach.The ML algorithm used was linear regression,applied to 27 attributes,21 of which were generated through feature engineering to enhance the model’s performance.The proposed solution demonstrates an R^(2) score greater than 0.99 in the estimation of both ESR and C.展开更多
Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for a...Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for almost 45%of all new cases worldwide^([2]).展开更多
According to some main assumptions in the Rouse Formula,it analyzes the applicability of Rouse distribution in the coastal region.Based on the classical Rouse Formula,the linear form of Rouse Formula and the transport...According to some main assumptions in the Rouse Formula,it analyzes the applicability of Rouse distribution in the coastal region.Based on the classical Rouse Formula,the linear form of Rouse Formula and the transport characteristics of offshore sediment were used to take lnz/h,lnc_(a),c_(a),u,lnu and z/h as the independent variables.The multiple liner regression method was used to analyze the influence of the independent variables on the vertical distribution of sediment concentration.By using the method of significance test,the factors(ln𝑢)that have less influence on sediment concentration among 6 variables were eliminated.The correlation coefficient between the calculated sediment concentration and the measured sediment concentration indicates that the adopted variables can reflect the characteristics of vertical distribution of concentration of fine sediment near shore under complex dynamic conditions.展开更多
This paper considers the approaches and methods for reducing the influence of multi-collinearity. Great attention is paid to the question of using shrinkage estimators for this purpose. Two classes of regression model...This paper considers the approaches and methods for reducing the influence of multi-collinearity. Great attention is paid to the question of using shrinkage estimators for this purpose. Two classes of regression models are investigated, the first of which corresponds to systems with a negative feedback, while the second class presents systems without the feedback. In the first case the use of shrinkage estimators, especially the Principal Component estimator, is inappropriate but is possible in the second case with the right choice of the regularization parameter or of the number of principal components included in the regression model. This fact is substantiated by the study of the distribution of the random variable , where b is the LS estimate and β is the true coefficient, since the form of this distribution is the basic characteristic of the specified classes. For this study, a regression approximation of the distribution of the event based on the Edgeworth series was developed. Also, alternative approaches are examined to resolve the multicollinearity issue, including an application of the known Inequality Constrained Least Squares method and the Dual estimator method proposed by the author. It is shown that with a priori information the Euclidean distance between the estimates and the true coefficients can be significantly reduced.展开更多
Model accuracy and runtime are two key issues for flood warnings in rivers.Traditional hydrodynamic models,which have a rigorous physical mechanism for flood routine,have been widely adopted for water level prediction...Model accuracy and runtime are two key issues for flood warnings in rivers.Traditional hydrodynamic models,which have a rigorous physical mechanism for flood routine,have been widely adopted for water level prediction in river,lake,and urban areas.However,these models require various types of data,in-depth domain knowledge,experience with modeling,and intensive computational time,which hinders short-term or real-time prediction.In this paper,we propose a new framework based on machine learning methods to alleviate the aforementioned limitation.We develop a wide range of machine learning models such as linear regression(LR),support vector regression(SVR),random forest regression(RFR),multilayer perceptron regression(MLPR),and light gradient boosting machine regression(LGBMR)to predict the hourly water level at Le Thuy and Kien Giang stations of the Kien Giang river based on collected data of 2010,2012,and 2020.Four evaluation metrics,that is,R^(2),Nash-Sutcliffe efficiency,mean absolute error,and root mean square error,are employed to examine the reliability of the proposed models.The results show that the LR model outperforms the SVR,RFR,MLPR,and LGBMR models.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
As maritime activities increase globally,there is a greater dependency on technology in monitoring,control,and surveillance of vessel activity.One of the most prominent systems for monitoring vessel activity is the Au...As maritime activities increase globally,there is a greater dependency on technology in monitoring,control,and surveillance of vessel activity.One of the most prominent systems for monitoring vessel activity is the Automatic Identification System(AIS).An increase in both vessels fitted with AIS transponders and satellite and terrestrial AIS receivers has resulted in a significant increase in AIS messages received globally.This resultant rich spatial and temporal data source related to vessel activity provides analysts with the ability to perform enhanced vessel movement analytics,of which a pertinent example is the improvement of vessel location predictions.In this paper,we propose a novel strategy for predicting future locations of vessels making use of historic AIS data.The proposed method uses a Linear Regression Model(LRM)and utilizes historic AIS movement data in the form of a-priori generated spatial maps of the course over ground(LRMAC).The LRMAC is an accurate low complexity first-order method that is easy to implement operationally and shows promising results in areas where there is a consistency in the directionality of historic vessel movement.In areas where the historic directionality of vessel movement is diverse,such as areas close to harbors and ports,the LRMAC defaults to the LRM.The proposed LRMAC method is compared to the Single-Point Neighbor Search(SPNS),which is also a first-order method and has a similar level of computational complexity,and for the use case of predicting tanker and cargo vessel trajectories up to 8 hours into the future,the LRMAC showed improved results both in terms of prediction accuracy and execution time.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
As one of the first coastal open cities in China,Yantai City is situated in the eastern Shandong Peninsula,bordered by the Yellow Sea and Bohai Sea.With the continuous improvement of tourism infrastructure,public enth...As one of the first coastal open cities in China,Yantai City is situated in the eastern Shandong Peninsula,bordered by the Yellow Sea and Bohai Sea.With the continuous improvement of tourism infrastructure,public enthusiasm for tourism in Yantai has been growing.To formulate more effective tourism development policies tailored to the local context,this study examines Yantai City using a multiple linear regression model to identify the primary factors influencing domestic tourism income.Based on the findings,this paper proposes scientifically grounded and actionable strategies to further optimize the development of tourism in Yantai City.展开更多
The rock matrix bulk modulus or its inverse, the compressive coefficient, is an important input parameter for fluid substitution by the Biot-Gassmann equation in reservoir prediction. However, it is not easy to accura...The rock matrix bulk modulus or its inverse, the compressive coefficient, is an important input parameter for fluid substitution by the Biot-Gassmann equation in reservoir prediction. However, it is not easy to accurately estimate the bulk modulus by using conventional methods. In this paper, we present a new linear regression equation for calculating the parameter. In order to get this equation, we first derive a simplified Gassmann equation by using a reasonable assumption in which the compressive coefficient of the saturated pore fluid is much greater than the rock matrix, and, second, we use the Eshelby- Walsh relation to replace the equivalent modulus of a dry rock in the Gassmann equation. Results from the rock physics analysis of rock sample from a carbonate area show that rock matrix compressive coefficients calculated with water-saturated and dry rock samples using the linear regression method are very close （their error is less than 1%）. This means the new method is accurate and reliable.展开更多
Pyrrolizidine alkaloids(PAs)and their N-oxides(PANOs)are phytotoxins produced by various plant species and have been emerged as environmental pollutants.The sorption/desorption behaviors of PAs/PANOs in soil are cruci...Pyrrolizidine alkaloids(PAs)and their N-oxides(PANOs)are phytotoxins produced by various plant species and have been emerged as environmental pollutants.The sorption/desorption behaviors of PAs/PANOs in soil are crucial due to the horizontal transfer of these natural products from PA-producing plants to soil and subsequently absorbed by plant roots.This study firstly investigated the sorption/desorption behaviors of PAs/PANOs in tea plantation soils with distinct characteristics.Sorption amounts for seneciphylline(Sp)and seneciphylline-N-oxide(SpNO)in three acidic soils ranged from 2.9 to 5.9μg/g and 1.7 to 2.8μg/g,respectively.Desorption percentages for Sp and SpNO were from 22.2%to 30.5%and 36.1%to 43.9%.In the mixed PAs/PANOs systems,stronger sorption of PAs over PANOs was occurred in tested soils.Additionally,the Freundlich models more precisely described the sorption/desorption isotherms.Cation exchange capacity,sand content and total nitrogen were identified as major influencing factors by linear regression models.Overall,the soils exhibiting higher sorption capacities for compounds with greater hydrophobicity.PANOs were more likely to migrate within soils and be absorbed by tea plants.It contributes to the understanding of environmental fate of PAs/PANOs in tea plantations and provides basic data and clues for the development of PAs/PANOs reduction technology.展开更多
As the traditional methods and technical means cannot meet the quantitative research needs of the urban land use patterns, quantitative research methods for the urban land use pattern are established via the GIS （geo...As the traditional methods and technical means cannot meet the quantitative research needs of the urban land use patterns, quantitative research methods for the urban land use pattern are established via the GIS （geographic information system ） technique combined with the related theories and models. Taking the city of Nanjing as an example, a spatial database of urban land use and other environmental and socio-economic data is constructed. A multiple linear regression model is developed to determine the statistically significant factors affecting the residential land use distributions. To explain the spatial variations of urban land use patterns, the geographically weighted regression （GWR） is employed to establish spatial associations between these significant factors and the distribution of urban residential land use. The results demonstrate that the GWR can provide an effective approach to the exploration of the urban land use spatial patterns and also provide useful spatial information for planning residential development and other types of urban land use.展开更多
Due to the heterogeneity of rock masses and the variability of in situ stress,the traditional linear inversion method is insufficiently accurate to achieve high accuracy of the in situ stress field.To address this cha...Due to the heterogeneity of rock masses and the variability of in situ stress,the traditional linear inversion method is insufficiently accurate to achieve high accuracy of the in situ stress field.To address this challenge,nonlinear stress boundaries for a numerical model are determined through regression analysis of a series of nonlinear coefficient matrices,which are derived from the bubbling method.Considering the randomness and flexibility of the bubbling method,a parametric study is conducted to determine recommended ranges for these parameters,including the standard deviation(σb)of bubble radii,the non-uniform coefficient matrix number(λ)for nonlinear stress boundaries,and the number(m)and positions of in situ stress measurement points.A model case study provides a reference for the selection of these parameters.Additionally,when the nonlinear in situ stress inversion method is employed,stress distortion inevitably occurs near model boundaries,aligning with the Saint Venant's principle.Two strategies are proposed accordingly:employing a systematic reduction of nonlinear coefficients to achieve high inversion accuracy while minimizing significant stress distortion,and excluding regions with severe stress distortion near the model edges while utilizing the central part of the model for subsequent simulations.These two strategies have been successfully implemented in the nonlinear in situ stress inversion of the Xincheng Gold Mine and have achieved higher inversion accuracy than the linear method.Specifically,the linear and nonlinear inversion methods yield root mean square errors(RMSE)of 4.15 and 3.2,and inversion relative errors(δAve)of 22.08%and 17.55%,respectively.Therefore,the nonlinear inversion method outperforms the traditional multiple linear regression method,even in the presence of a systematic reduction in the nonlinear stress boundaries.展开更多
Strong sensitivity of satellite microwave remote sensing to the change of surface dielectric properties,as well as the insensitivity to air pollution and solar illumination effects,makes it very suitable for monitorin...Strong sensitivity of satellite microwave remote sensing to the change of surface dielectric properties,as well as the insensitivity to air pollution and solar illumination effects,makes it very suitable for monitoring freeze-thaw conditions.The freeze-thaw cycle changes in the Qinghai-Xizang Plateau have an important impact on the ecological environment and infrastructure.Based on the Scanning Multi-channel Microwave Radiometer(SMMR)and other sensors of microwave satellite,the freeze-thaw cycle data of permafrost in the Qinghai-Xizang Plateau in the past 40 years from 1981 to 2020 was obtained.The changes of soil freeze-thaw conditions in different seasons of 2020 and in the same season of 1990,2000,2010 and 2020 were compared,and the annual variation trend of soil freeze-thaw area in the four years was analyzed.Further,the linear regression analysis was carried out on the duration of soil freezing/thawing/transition and the interannual variation trend under different area conditions from 1981 to 2020.The results show that the freeze-thaw changes in different years are similar.In winter,it is mainly frozen for about 110 days.Spring and autumn are transitional periods,lasting for 170 days.In summer,it is mainly thawed for about 80 days.From 1981 to 2020,the freezing period and the average freezing area of the Qinghai-Xizang Plateau decreased at a rate of 0.22 days and 1986 km^(2) per year,respectively,while the thawing period and the average thawing area increased at a rate of 0.07 days and 3187 km^(2) per year,respectively.The research results provide important theoretical support for the ecological environment and permafrost protection of the Qinghai-Xizang Plateau.展开更多
High-dimensional data(a dataset with many features)were collected from 64 sampling sites to analyze the water quality in estuaries along the coast of the Bohai Sea,North China.The twenty-five water quality parameters ...High-dimensional data(a dataset with many features)were collected from 64 sampling sites to analyze the water quality in estuaries along the coast of the Bohai Sea,North China.The twenty-five water quality parameters analyzed were collected monthly from January 2021 to December 2021.Multivariate statistical techniques,such as the absolute principal component score-multiple linear regression model(APCS-MLR),correlation analysis,and analysis of variance were used to identify and quantify the potential sources or factors affecting water quality and to analyze the spatial-temporal variation in water quality.The water quality indices(WQIs),ranging from 67.96 to 70.67,showed that the water quality was at an intermediate level in the estuaries during both the flood and nonflood seasons.The concentrations of total phosphorus(TP),ammonia N(AN),and organic pollutants were greater in the Haihe River Basin than in the Liaohe River and Huanghe-Huaihe River Basins.The concentration of total nitrogen(TN)in the Haihe River Basin was lower than that in the Liaohe River and Huanghe-Huaihe River Basins.Heavy metal concentrations in the Liaohe River Basin were greater than those in the Haihe River and Huanghe-Huaihe River Basins.The annual mean concentrations of AN in the estuaries of the Haihe,Liaohe,and Huanghe(Yellow)rivers exhibited significant decreasing trends from 2013 to 2022,but no significant decreasing trends were found for permanganate index(COD_(Mn))or the TP.The concentrations of TN and AN were lower in the flood season than in the nonflood season,and the TP concentration was greater in the flood season than in the nonflood season.However,the concentrations of organic pollutants did not exhibit significant differences.Domestic sewage and industrial wastewater,substance exchange between air and water,nonpoint sources from rural and urban areas,and aquaculture wastewater were the major sources or factors responsible for water pollution in the estuaries.展开更多
The relationship between hyperuricemia(HUA)and erectile dysfunction(ED)remains inadequately understood.Given that HUA is often associated with various metabolic disorders,this study aims to explore the multivariate li...The relationship between hyperuricemia(HUA)and erectile dysfunction(ED)remains inadequately understood.Given that HUA is often associated with various metabolic disorders,this study aims to explore the multivariate linear impacts of metabolic parameters on erectile function in ED patients with HUA.A cross-sectional analysis was conducted involving 514 ED patients with HUA in the Department of Andrology,Jiangsu Province Hospital of Chinese Medicine(Nanjing,China),aged 18 to 60 years.General demographic information,medical history,and laboratory results were collected to assess metabolic disturbances.Sexual function was evaluated using the 5-item version of the International Index of Erectile Function(IIEF-5)questionnaire.Based on univariate analysis,variables associated with IIEF-5 scores were identified,and the correlations between them were evaluated.The effects of these variables on IIEF-5 scores were further explored by multiple linear regression models.Fasting plasma glucose(β=−0.628,P<0.001),uric acid(β=−0.552,P<0.001),triglycerides(β=−0.088,P=0.047),low-density lipoprotein cholesterol(β=−0.164,P=0.027),glycated hemoglobin(HbA1c;β=−0.562,P=0.012),and smoking history(β=−0.074,P=0.037)exhibited significant negative impacts on erectile function.The coefficient of determination(R²)for the model was 0.239,and the adjusted R²was 0.230,indicating overall statistical significance(F-statistic=26.52,P<0.001).Metabolic parameters play a crucial role in the development of ED.Maintaining normal metabolic indices may aid in the prevention and improvement of erectile function in ED patients with HUA.展开更多
The precision of dynamic reserve calculations in gas reservoirs is crucial for the rational and efficient development of oil and gas fields and the formulation of gas well production plans.The Shaximiao gas reservoir ...The precision of dynamic reserve calculations in gas reservoirs is crucial for the rational and efficient development of oil and gas fields and the formulation of gas well production plans.The Shaximiao gas reservoir in the ZT block of northwestern Sichuan is densely packed and highly heterogeneous,featuring complex gas-water distribution,substantial variations in test production among gas wells,and a rapid decline rate.To precisely determine the dynamic reserves of these tight water-bearing gas wells,this study focuses on the water-tight gas reservoirs in the ZT block of northwestern Sichuan,conducting core X-ray diffraction,constant-rate mercury injection,and reservoir rock stress sensitivity experiments.Utilizing the experimental findings,the porosity and permeability of the rock samples under effective stress conditions are adjusted via binary linear regression.These adjusted parameters are then incorporated into the water-sealed gas material balance method,thereby establishing a novel approach for calculating dynamic reserves in water-tight gas reservoirs under stress sensitivity conditions.The results show that:(1)the rock porosity ranges from 6.08%to 10.22%,permeability ranges from 0.035 mD to 0.547 mD,clay mineral content ranges from 6.58%to 19.14%,pore radius distribution ranges from 90μm to 180μm,throat radius distribution ranges from 0.61μm to 3.41μm,with significant differences in throat distribution,indicating poor reservoir fluid flow capacity and strong tightness;(2)after aging experiments,rock samples exhibit plastic deformation,with porosity and permeability unable to fully recover after pressure relief.The stress sensitivity curve of rock samples shows a two-stage characteristic,with moderate to strong stress sensitivity;(3)porosity stress sensitivity is mainly influenced by pore radius and mineral composition-larger pore radius and higher clay content lead to stronger stress sensitivity,with porosity loss rates ranging from 8.26%to 23.69%.Permeability stress sensitivity is mainly influenced by throat radius and mineral composition-smaller throat radius and higher clay content result in stronger stress sensitivity,with permeability loss rates ranging from 47.91%to 62.03%;(4)a comparative analysis between the traditional dynamic reserve calculation method for gas wells and the new method considering stress sensitivity shows a relative error between 0.90%and 2.41%,with the new method demonstrating better accuracy.This study combines physical experimental results with an effective stress model of reservoir rocks to develop a new method for calculating dynamic reserves of water-bearing tight gas reservoirs under effective stress conditions,providing experimental data and example calculation results to support subsequent dynamic evaluation of gas reservoirs and the establishment of rational well allocation plans.展开更多
Biomass models to estimate carbon stocks in arid environment are very limited. This study employed destructive sampling to develop a new biomass model for Vachellia tortilis, a widely known species in the Sultanate of...Biomass models to estimate carbon stocks in arid environment are very limited. This study employed destructive sampling to develop a new biomass model for Vachellia tortilis, a widely known species in the Sultanate of Oman. Twenty trees with a diameter at stump height (DSH) ranging from 18.5 cm to 150 cm were selected based on DSH and height variations for destructive sampling in As Saleel Natural Park Reserve (SNPR) in Al Sharqiyah governorate, South of Oman. Each tree was excavated and cut into three parts: Stems, Branches, twigs, and leaves. The total fresh weight of each tree was obtained in the field using a 300 balance. Sub-samples (250 - 300 grams) were taken from each part of the tree and transferred to the laboratory for dry weight determination. Linear multiple regression analysis was done using SPSS software between the three variables, DSH, H, CA (x) and the total dry biomass (y). Five models were tested for the best-fit model based on R-Square and Mean Square Error (MSE). Model 5 was the best-fit model, including the LOG of DSH and the LOG of CA (R2 = 0.97, MSE = 0.114). The models developed in this research fill a critical gap in estimating the AGB of terrestrial native species in Oman and other countries with similar ecological and climate conditions.展开更多
文摘Power converters are essential components in modern life,being widely used in industry,automation,transportation,and household appliances.In many critical applications,their failure can lead not only to financial losses due to operational downtime but also to serious risks to human safety.The capacitors forming the output filter,typically aluminumelectrolytic capacitors(AECs),are among the most critical and susceptible components in power converters.The electrolyte in AECs often evaporates over time,causing the internal resistance to rise and the capacitance to drop,ultimately leading to component failure.Detecting this fault requires measuring the current in the capacitor,rendering the method invasive and frequently impractical due to spatial constraints or operational limitations imposed by the integration of a current sensor in the capacitor branch.This article proposes the implementation of an online noninvasive fault diagnosis technique for estimating the Equivalent Series Resistance(ESR)and Capacitance(C)values of the capacitor,employing a combination of signal processing techniques(SPT)and machine learning(ML)algorithms.This solution relies solely on the converter’s input and output signals,therefore making it a non-invasive approach.The ML algorithm used was linear regression,applied to 27 attributes,21 of which were generated through feature engineering to enhance the model’s performance.The proposed solution demonstrates an R^(2) score greater than 0.99 in the estimation of both ESR and C.
基金supported by the Natural Science Foundation of Shanghai(23ZR1463600)Shanghai Pudong New Area Health Commission Research Project(PW2021A-69)Research Project of Clinical Research Center of Shanghai Health Medical University(22MC2022002)。
文摘Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for almost 45%of all new cases worldwide^([2]).
文摘According to some main assumptions in the Rouse Formula,it analyzes the applicability of Rouse distribution in the coastal region.Based on the classical Rouse Formula,the linear form of Rouse Formula and the transport characteristics of offshore sediment were used to take lnz/h,lnc_(a),c_(a),u,lnu and z/h as the independent variables.The multiple liner regression method was used to analyze the influence of the independent variables on the vertical distribution of sediment concentration.By using the method of significance test,the factors(ln𝑢)that have less influence on sediment concentration among 6 variables were eliminated.The correlation coefficient between the calculated sediment concentration and the measured sediment concentration indicates that the adopted variables can reflect the characteristics of vertical distribution of concentration of fine sediment near shore under complex dynamic conditions.
文摘This paper considers the approaches and methods for reducing the influence of multi-collinearity. Great attention is paid to the question of using shrinkage estimators for this purpose. Two classes of regression models are investigated, the first of which corresponds to systems with a negative feedback, while the second class presents systems without the feedback. In the first case the use of shrinkage estimators, especially the Principal Component estimator, is inappropriate but is possible in the second case with the right choice of the regularization parameter or of the number of principal components included in the regression model. This fact is substantiated by the study of the distribution of the random variable , where b is the LS estimate and β is the true coefficient, since the form of this distribution is the basic characteristic of the specified classes. For this study, a regression approximation of the distribution of the event based on the Edgeworth series was developed. Also, alternative approaches are examined to resolve the multicollinearity issue, including an application of the known Inequality Constrained Least Squares method and the Dual estimator method proposed by the author. It is shown that with a priori information the Euclidean distance between the estimates and the true coefficients can be significantly reduced.
基金Scientific Research and Technology Development Project。
文摘Model accuracy and runtime are two key issues for flood warnings in rivers.Traditional hydrodynamic models,which have a rigorous physical mechanism for flood routine,have been widely adopted for water level prediction in river,lake,and urban areas.However,these models require various types of data,in-depth domain knowledge,experience with modeling,and intensive computational time,which hinders short-term or real-time prediction.In this paper,we propose a new framework based on machine learning methods to alleviate the aforementioned limitation.We develop a wide range of machine learning models such as linear regression(LR),support vector regression(SVR),random forest regression(RFR),multilayer perceptron regression(MLPR),and light gradient boosting machine regression(LGBMR)to predict the hourly water level at Le Thuy and Kien Giang stations of the Kien Giang river based on collected data of 2010,2012,and 2020.Four evaluation metrics,that is,R^(2),Nash-Sutcliffe efficiency,mean absolute error,and root mean square error,are employed to examine the reliability of the proposed models.The results show that the LR model outperforms the SVR,RFR,MLPR,and LGBMR models.
文摘In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging.Through a rigorous assessment,we explore the efficacy of eight regression models,bifurcated into linear and nonlinear groups,to accommodate the multifaceted nature of geological datasets.Our linear model suite encompasses the Standard Equation,Ridge Regression,Least Absolute Shrinkage and Selection Operator,and Elastic Net,each presenting distinct advantages.The Standard Equation serves as a foundational benchmark,whereas Ridge Regression implements penalty terms to counteract overfitting,thus bolstering model robustness in the presence of multicollinearity.The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models,enhancing their interpretability,while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator,offering a harmonized solution to model complexity and comprehensibility.On the nonlinear front,Gradient Descent,Kernel Ridge Regression,Support Vector Regression,and Piecewise Function-Fitting methods introduce innovative approaches.Gradient Descent assures computational efficiency in optimizing solutions,Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns,and Support Vector Regression is proficient in forecasting extremities,pivotal for exploration risk assessment.The Piecewise Function-Fitting approach,tailored for geological data,facilitates adaptable modeling of variable interrelations,accommodating abrupt data trend shifts.Our analysis identifies Ridge Regression,particularly when augmented by Piecewise Function-Fitting,as superior in recouping hydrocarbon losses,and underscoring its utility in resource quantification refinement.Meanwhile,Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A,evidencing its aptness for intricate geological structures.This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector.The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction,evaluation,and recovery.
文摘As maritime activities increase globally,there is a greater dependency on technology in monitoring,control,and surveillance of vessel activity.One of the most prominent systems for monitoring vessel activity is the Automatic Identification System(AIS).An increase in both vessels fitted with AIS transponders and satellite and terrestrial AIS receivers has resulted in a significant increase in AIS messages received globally.This resultant rich spatial and temporal data source related to vessel activity provides analysts with the ability to perform enhanced vessel movement analytics,of which a pertinent example is the improvement of vessel location predictions.In this paper,we propose a novel strategy for predicting future locations of vessels making use of historic AIS data.The proposed method uses a Linear Regression Model(LRM)and utilizes historic AIS movement data in the form of a-priori generated spatial maps of the course over ground(LRMAC).The LRMAC is an accurate low complexity first-order method that is easy to implement operationally and shows promising results in areas where there is a consistency in the directionality of historic vessel movement.In areas where the historic directionality of vessel movement is diverse,such as areas close to harbors and ports,the LRMAC defaults to the LRM.The proposed LRMAC method is compared to the Single-Point Neighbor Search(SPNS),which is also a first-order method and has a similar level of computational complexity,and for the use case of predicting tanker and cargo vessel trajectories up to 8 hours into the future,the LRMAC showed improved results both in terms of prediction accuracy and execution time.
基金2024 Guangdong Philosophy and Social Science Planning Discipline Co-construction Project“Study on the Measurement of Economic Benefits and Path of High-Quality Development of Museums in Guangdong Province”(Project No.GD24XYS045)Key Project of the Social Sciences Division of Shenzhen Polytechnic University“Research on Strategies for Enhancing the Effectiveness of Non-State-Owned Museums in Shenzhen”(Project No.20240105)+1 种基金Shenzhen Polytechnic University’s Platform Construction Project“SZPU-Fangzhi Technology AI New Media R&D Centre”(Project No:602331019PQ)Open-ended Project of the Global Urban Civilization Model Research Institute of Southern University of Science and Technology in 2024,“Research on the Efficiency Enhancement Strategy of Non State owned Museums in Shenzhen from the Perspective of Urban Civilization Construction”(Project No.IGUC24C011)。
文摘The purpose of this research is to explore the factors influencing the self-improvement process of museums in China and to conduct empirical analyses based on multiple linear regression models.As core institutions for inheriting and displaying cultural heritage and enhancing public cultural literacy,museums’self-improvement is of great significance in promoting cultural development,optimizing the supply of public cultural services,and enhancing social influence.This paper constructs a multiple linear regression model for the influencing factors of museum self-improvement by integrating several key variables,including emerging cultural and museum business(EF),institutional reform(SR),research and innovation level(RIL),management level(ML),and the museum cultural and creative industry(MCCI).The study employs scientific methods such as literature review,data collection,and data analysis to thoroughly explore the internal logic of museum operations and development.Through multiple linear regression analyses,it quantifies the specific influence and relative importance of each factor on the level of museum self-improvement.The results indicate that the management level(ML)is the dominant factor among the variables studied,exerting the most significant influence on museum self-improvement.Based on these empirical findings,this paper provides an in-depth analysis of the specific factors affecting museum self-improvement in China,offering solid theoretical support and practical guidance for the sustainable development of museums.
文摘As one of the first coastal open cities in China,Yantai City is situated in the eastern Shandong Peninsula,bordered by the Yellow Sea and Bohai Sea.With the continuous improvement of tourism infrastructure,public enthusiasm for tourism in Yantai has been growing.To formulate more effective tourism development policies tailored to the local context,this study examines Yantai City using a multiple linear regression model to identify the primary factors influencing domestic tourism income.Based on the findings,this paper proposes scientifically grounded and actionable strategies to further optimize the development of tourism in Yantai City.
基金supported by the National Nature Science Foundation of China (Grant Noss 40739907 and 40774064)National Science and Technology Major Project (Grant No. 2008ZX05025-003)
文摘The rock matrix bulk modulus or its inverse, the compressive coefficient, is an important input parameter for fluid substitution by the Biot-Gassmann equation in reservoir prediction. However, it is not easy to accurately estimate the bulk modulus by using conventional methods. In this paper, we present a new linear regression equation for calculating the parameter. In order to get this equation, we first derive a simplified Gassmann equation by using a reasonable assumption in which the compressive coefficient of the saturated pore fluid is much greater than the rock matrix, and, second, we use the Eshelby- Walsh relation to replace the equivalent modulus of a dry rock in the Gassmann equation. Results from the rock physics analysis of rock sample from a carbonate area show that rock matrix compressive coefficients calculated with water-saturated and dry rock samples using the linear regression method are very close （their error is less than 1%）. This means the new method is accurate and reliable.
基金supported by the earmarked fund for the Modern Agro-Industry Technology Research System (No.CARS-19)the Innovative Research Team in Chinese Academy of Agricultural Sciences (No.CAAS ASTIP-2014-TRICAAS).
文摘Pyrrolizidine alkaloids(PAs)and their N-oxides(PANOs)are phytotoxins produced by various plant species and have been emerged as environmental pollutants.The sorption/desorption behaviors of PAs/PANOs in soil are crucial due to the horizontal transfer of these natural products from PA-producing plants to soil and subsequently absorbed by plant roots.This study firstly investigated the sorption/desorption behaviors of PAs/PANOs in tea plantation soils with distinct characteristics.Sorption amounts for seneciphylline(Sp)and seneciphylline-N-oxide(SpNO)in three acidic soils ranged from 2.9 to 5.9μg/g and 1.7 to 2.8μg/g,respectively.Desorption percentages for Sp and SpNO were from 22.2%to 30.5%and 36.1%to 43.9%.In the mixed PAs/PANOs systems,stronger sorption of PAs over PANOs was occurred in tested soils.Additionally,the Freundlich models more precisely described the sorption/desorption isotherms.Cation exchange capacity,sand content and total nitrogen were identified as major influencing factors by linear regression models.Overall,the soils exhibiting higher sorption capacities for compounds with greater hydrophobicity.PANOs were more likely to migrate within soils and be absorbed by tea plants.It contributes to the understanding of environmental fate of PAs/PANOs in tea plantations and provides basic data and clues for the development of PAs/PANOs reduction technology.
基金The National Natural Science Foundation of China(No.51378099)
文摘As the traditional methods and technical means cannot meet the quantitative research needs of the urban land use patterns, quantitative research methods for the urban land use pattern are established via the GIS （geographic information system ） technique combined with the related theories and models. Taking the city of Nanjing as an example, a spatial database of urban land use and other environmental and socio-economic data is constructed. A multiple linear regression model is developed to determine the statistically significant factors affecting the residential land use distributions. To explain the spatial variations of urban land use patterns, the geographically weighted regression （GWR） is employed to establish spatial associations between these significant factors and the distribution of urban residential land use. The results demonstrate that the GWR can provide an effective approach to the exploration of the urban land use spatial patterns and also provide useful spatial information for planning residential development and other types of urban land use.
基金funded by the National Key R&D Program of China(Grant No.2022YFC2903904)the National Natural Science Foundation of China(Grant Nos.51904057 and U1906208).
文摘Due to the heterogeneity of rock masses and the variability of in situ stress,the traditional linear inversion method is insufficiently accurate to achieve high accuracy of the in situ stress field.To address this challenge,nonlinear stress boundaries for a numerical model are determined through regression analysis of a series of nonlinear coefficient matrices,which are derived from the bubbling method.Considering the randomness and flexibility of the bubbling method,a parametric study is conducted to determine recommended ranges for these parameters,including the standard deviation(σb)of bubble radii,the non-uniform coefficient matrix number(λ)for nonlinear stress boundaries,and the number(m)and positions of in situ stress measurement points.A model case study provides a reference for the selection of these parameters.Additionally,when the nonlinear in situ stress inversion method is employed,stress distortion inevitably occurs near model boundaries,aligning with the Saint Venant's principle.Two strategies are proposed accordingly:employing a systematic reduction of nonlinear coefficients to achieve high inversion accuracy while minimizing significant stress distortion,and excluding regions with severe stress distortion near the model edges while utilizing the central part of the model for subsequent simulations.These two strategies have been successfully implemented in the nonlinear in situ stress inversion of the Xincheng Gold Mine and have achieved higher inversion accuracy than the linear method.Specifically,the linear and nonlinear inversion methods yield root mean square errors(RMSE)of 4.15 and 3.2,and inversion relative errors(δAve)of 22.08%and 17.55%,respectively.Therefore,the nonlinear inversion method outperforms the traditional multiple linear regression method,even in the presence of a systematic reduction in the nonlinear stress boundaries.
基金National Natural Science foundation of China(No.42271432)Foundation of Shanxi Vocational University of Engineering Science and Technology(No.KJ 202426).
文摘Strong sensitivity of satellite microwave remote sensing to the change of surface dielectric properties,as well as the insensitivity to air pollution and solar illumination effects,makes it very suitable for monitoring freeze-thaw conditions.The freeze-thaw cycle changes in the Qinghai-Xizang Plateau have an important impact on the ecological environment and infrastructure.Based on the Scanning Multi-channel Microwave Radiometer(SMMR)and other sensors of microwave satellite,the freeze-thaw cycle data of permafrost in the Qinghai-Xizang Plateau in the past 40 years from 1981 to 2020 was obtained.The changes of soil freeze-thaw conditions in different seasons of 2020 and in the same season of 1990,2000,2010 and 2020 were compared,and the annual variation trend of soil freeze-thaw area in the four years was analyzed.Further,the linear regression analysis was carried out on the duration of soil freezing/thawing/transition and the interannual variation trend under different area conditions from 1981 to 2020.The results show that the freeze-thaw changes in different years are similar.In winter,it is mainly frozen for about 110 days.Spring and autumn are transitional periods,lasting for 170 days.In summer,it is mainly thawed for about 80 days.From 1981 to 2020,the freezing period and the average freezing area of the Qinghai-Xizang Plateau decreased at a rate of 0.22 days and 1986 km^(2) per year,respectively,while the thawing period and the average thawing area increased at a rate of 0.07 days and 3187 km^(2) per year,respectively.The research results provide important theoretical support for the ecological environment and permafrost protection of the Qinghai-Xizang Plateau.
基金Supported by the National Natural Science Foundation of China(No.41571479)。
文摘High-dimensional data(a dataset with many features)were collected from 64 sampling sites to analyze the water quality in estuaries along the coast of the Bohai Sea,North China.The twenty-five water quality parameters analyzed were collected monthly from January 2021 to December 2021.Multivariate statistical techniques,such as the absolute principal component score-multiple linear regression model(APCS-MLR),correlation analysis,and analysis of variance were used to identify and quantify the potential sources or factors affecting water quality and to analyze the spatial-temporal variation in water quality.The water quality indices(WQIs),ranging from 67.96 to 70.67,showed that the water quality was at an intermediate level in the estuaries during both the flood and nonflood seasons.The concentrations of total phosphorus(TP),ammonia N(AN),and organic pollutants were greater in the Haihe River Basin than in the Liaohe River and Huanghe-Huaihe River Basins.The concentration of total nitrogen(TN)in the Haihe River Basin was lower than that in the Liaohe River and Huanghe-Huaihe River Basins.Heavy metal concentrations in the Liaohe River Basin were greater than those in the Haihe River and Huanghe-Huaihe River Basins.The annual mean concentrations of AN in the estuaries of the Haihe,Liaohe,and Huanghe(Yellow)rivers exhibited significant decreasing trends from 2013 to 2022,but no significant decreasing trends were found for permanganate index(COD_(Mn))or the TP.The concentrations of TN and AN were lower in the flood season than in the nonflood season,and the TP concentration was greater in the flood season than in the nonflood season.However,the concentrations of organic pollutants did not exhibit significant differences.Domestic sewage and industrial wastewater,substance exchange between air and water,nonpoint sources from rural and urban areas,and aquaculture wastewater were the major sources or factors responsible for water pollution in the estuaries.
基金supported by Jiangsu Provincial Science and Technology Plan Special Project(No.BK20231379)Key Project of Jiangsu Provincial Health Commission(No.ZDA2020025)+1 种基金Jiangsu Traditional Chinese Medicine Science and Technology Development Plan Project(No.MS2022023)Excellent Young Doctor Training Program of Jiangsu Province Hospital of Chinese Medicine(No.2023QB0126).
文摘The relationship between hyperuricemia(HUA)and erectile dysfunction(ED)remains inadequately understood.Given that HUA is often associated with various metabolic disorders,this study aims to explore the multivariate linear impacts of metabolic parameters on erectile function in ED patients with HUA.A cross-sectional analysis was conducted involving 514 ED patients with HUA in the Department of Andrology,Jiangsu Province Hospital of Chinese Medicine(Nanjing,China),aged 18 to 60 years.General demographic information,medical history,and laboratory results were collected to assess metabolic disturbances.Sexual function was evaluated using the 5-item version of the International Index of Erectile Function(IIEF-5)questionnaire.Based on univariate analysis,variables associated with IIEF-5 scores were identified,and the correlations between them were evaluated.The effects of these variables on IIEF-5 scores were further explored by multiple linear regression models.Fasting plasma glucose(β=−0.628,P<0.001),uric acid(β=−0.552,P<0.001),triglycerides(β=−0.088,P=0.047),low-density lipoprotein cholesterol(β=−0.164,P=0.027),glycated hemoglobin(HbA1c;β=−0.562,P=0.012),and smoking history(β=−0.074,P=0.037)exhibited significant negative impacts on erectile function.The coefficient of determination(R²)for the model was 0.239,and the adjusted R²was 0.230,indicating overall statistical significance(F-statistic=26.52,P<0.001).Metabolic parameters play a crucial role in the development of ED.Maintaining normal metabolic indices may aid in the prevention and improvement of erectile function in ED patients with HUA.
基金supported by CNPC Southwest Oil and Gas Field Branch's 2023 Scientific Research Program Project(20230303-14).
文摘The precision of dynamic reserve calculations in gas reservoirs is crucial for the rational and efficient development of oil and gas fields and the formulation of gas well production plans.The Shaximiao gas reservoir in the ZT block of northwestern Sichuan is densely packed and highly heterogeneous,featuring complex gas-water distribution,substantial variations in test production among gas wells,and a rapid decline rate.To precisely determine the dynamic reserves of these tight water-bearing gas wells,this study focuses on the water-tight gas reservoirs in the ZT block of northwestern Sichuan,conducting core X-ray diffraction,constant-rate mercury injection,and reservoir rock stress sensitivity experiments.Utilizing the experimental findings,the porosity and permeability of the rock samples under effective stress conditions are adjusted via binary linear regression.These adjusted parameters are then incorporated into the water-sealed gas material balance method,thereby establishing a novel approach for calculating dynamic reserves in water-tight gas reservoirs under stress sensitivity conditions.The results show that:(1)the rock porosity ranges from 6.08%to 10.22%,permeability ranges from 0.035 mD to 0.547 mD,clay mineral content ranges from 6.58%to 19.14%,pore radius distribution ranges from 90μm to 180μm,throat radius distribution ranges from 0.61μm to 3.41μm,with significant differences in throat distribution,indicating poor reservoir fluid flow capacity and strong tightness;(2)after aging experiments,rock samples exhibit plastic deformation,with porosity and permeability unable to fully recover after pressure relief.The stress sensitivity curve of rock samples shows a two-stage characteristic,with moderate to strong stress sensitivity;(3)porosity stress sensitivity is mainly influenced by pore radius and mineral composition-larger pore radius and higher clay content lead to stronger stress sensitivity,with porosity loss rates ranging from 8.26%to 23.69%.Permeability stress sensitivity is mainly influenced by throat radius and mineral composition-smaller throat radius and higher clay content result in stronger stress sensitivity,with permeability loss rates ranging from 47.91%to 62.03%;(4)a comparative analysis between the traditional dynamic reserve calculation method for gas wells and the new method considering stress sensitivity shows a relative error between 0.90%and 2.41%,with the new method demonstrating better accuracy.This study combines physical experimental results with an effective stress model of reservoir rocks to develop a new method for calculating dynamic reserves of water-bearing tight gas reservoirs under effective stress conditions,providing experimental data and example calculation results to support subsequent dynamic evaluation of gas reservoirs and the establishment of rational well allocation plans.
文摘Biomass models to estimate carbon stocks in arid environment are very limited. This study employed destructive sampling to develop a new biomass model for Vachellia tortilis, a widely known species in the Sultanate of Oman. Twenty trees with a diameter at stump height (DSH) ranging from 18.5 cm to 150 cm were selected based on DSH and height variations for destructive sampling in As Saleel Natural Park Reserve (SNPR) in Al Sharqiyah governorate, South of Oman. Each tree was excavated and cut into three parts: Stems, Branches, twigs, and leaves. The total fresh weight of each tree was obtained in the field using a 300 balance. Sub-samples (250 - 300 grams) were taken from each part of the tree and transferred to the laboratory for dry weight determination. Linear multiple regression analysis was done using SPSS software between the three variables, DSH, H, CA (x) and the total dry biomass (y). Five models were tested for the best-fit model based on R-Square and Mean Square Error (MSE). Model 5 was the best-fit model, including the LOG of DSH and the LOG of CA (R2 = 0.97, MSE = 0.114). The models developed in this research fill a critical gap in estimating the AGB of terrestrial native species in Oman and other countries with similar ecological and climate conditions.
