During the process of coal prospecting and exploration, different measurement time, different logging instruments and series can lead to systematic errors in well logs. Accordingly, all logging curves need to be norma...During the process of coal prospecting and exploration, different measurement time, different logging instruments and series can lead to systematic errors in well logs. Accordingly, all logging curves need to be normalized in the mining area. By studying well-logging normalization methods, and focusing on the characteristics of the coalfield, the frequency histogram method was used in accordance with the condition of the Guqiao Coal Mine. In this way, the density and sonic velocity at marker bed in the non-key well were made to close to those in the key well, and were eventually equal. Well log normalization was completed when this method was applied to the entire logging curves. The results show that the scales of logging data were unified by normalizing coal logging curves, and the logging data were consistent with wave impedance inversion data. A satisfactory inversion effect was obtained.展开更多
AIM:To investigate the efficiency of Cox proportional hazard model in detecting prognostic factors for gastric cancer.METHODS:We used the log-normal regression model to evaluate prognostic factors in gastric cancer an...AIM:To investigate the efficiency of Cox proportional hazard model in detecting prognostic factors for gastric cancer.METHODS:We used the log-normal regression model to evaluate prognostic factors in gastric cancer and compared it with the Cox model.Three thousand and eighteen gastric cancer patients who received a gastrectomy between 1980 and 2004 were retrospectively evaluated.Clinic-pathological factors were included in a log-normal model as well as Cox model.The akaike information criterion (AIC) was employed to compare the efficiency of both models.Univariate analysis indicated that age at diagnosis,past history,cancer location,distant metastasis status,surgical curative degree,combined other organ resection,Borrmann type,Lauren's classification,pT stage,total dissected nodes and pN stage were prognostic factors in both log-normal and Cox models.RESULTS:In the final multivariate model,age at diagnosis,past history,surgical curative degree,Borrmann type,Lauren's classification,pT stage,and pN stage were significant prognostic factors in both log-normal and Cox models.However,cancer location,distant metastasis status,and histology types were found to be significant prognostic factors in log-normal results alone.According to AIC,the log-normal model performed better than the Cox proportional hazard model (AIC value:2534.72 vs 1693.56).CONCLUSION:It is suggested that the log-normal regression model can be a useful statistical model to evaluate prognostic factors instead of the Cox proportional hazard model.展开更多
Voltage scaling has been extensively used in industry for decades to reduce power consumption.In recent years,exploring digital circuit operation in moderate inversion has created an interest among researchers due to ...Voltage scaling has been extensively used in industry for decades to reduce power consumption.In recent years,exploring digital circuit operation in moderate inversion has created an interest among researchers due to its immense capability to provide a perfect tradeoff between high performance and low energy operation.But circuits operating in moderate inversion are susceptible to process variations and variability.To compute variability,statistical parameters such as the probability density function(PDF)and cumulative distribution function(CDF)are required.This paper presents an analytical model framework for delay calculations utilizing log skew normal distribution for ultradeep submicron technology nodes up to 22 nm.The CDF of the proposed model is utilized to calculate minimum and maximum delays with 3σ-accuracy providing better accuracy than the conventional methods.The obtained results are also compared with Monte Carlo simulations with errors lying within the acceptable range of 2%-4%.展开更多
A large number of particulate size distributions of welding aerosols are measured by means of DMPS method, several distribution types are presented. Among them the single peak distribution is the basic composing unit...A large number of particulate size distributions of welding aerosols are measured by means of DMPS method, several distribution types are presented. Among them the single peak distribution is the basic composing unit of particulate size. The research on the mathematic models and distributions functions shows that the single peak distribution features the log normal distribution. The diagram estimating method (DEM) is a concise approach to dealing with distribution types, obtaining distribution functions for the particulate sizes of welding aerosols. It proves that the distribution function of particulate size possesses the extending property, being from quantity distribution to volume, as well as high order moment distributions, with K S method verifying the application of single peak distribution and of DEM.展开更多
This paper aims at the spatiotemporal distribution of rainfall in Ethiopia and developing stochastic daily rainfall model.Particularly,in this study,we used a Markov Chain Analogue Year(MCAY)model that is,Markov Chain...This paper aims at the spatiotemporal distribution of rainfall in Ethiopia and developing stochastic daily rainfall model.Particularly,in this study,we used a Markov Chain Analogue Year(MCAY)model that is,Markov Chain with Analogue year(AY)component is used to model the occurrence process of daily rainfall and the intensity or amount of rainfall on wet days is described using Weibull,Log normal,mixed exponential and Gamma distributions.The MCAY model best describes the occurrence process of daily rainfall,this is due to the AY component included in the MC to model the frequency of daily rainfall.Then,by combining the occurrence process model and amount process model,we developed Markov Chain Analogue Year Weibull model(MCAYWBM),Markov Chain Analogue Year Log normal model(MCAYLNM),Markov Chain Analogue Year mixed exponential model(MCAYMEM)and Markov Chain Analogue Year gamma model(MCAYGM).The performance of the models is assessed by taking daily rainfall data from 21 weather stations(ranging from 1 January 1984–31 December 2018).The data is obtained from Ethiopia National Meteorology Agency(ENMA).The result shows that MCAYWBM,MCAYMEM and MCAYGM performs very well in the simulation of daily rainfall process in Ethiopia and their performances are nearly the same with a slight difference between them compared to MCAYLNM.The mean absolute percentage error(MAPE)in the four models:MCAYGM,MCAYWBM,MAYMEM and MCAYLNM are 2.16%,2.27%,2.25%and 11.41%respectively.Hence,MCAYGM,MCAYWBM,MAYMEM models have shown an excellent performance compared to MCAYLNM.In general,the light tailed distributions:Weibull,gamma and mixed exponential distributions are appropriate probability distributions to model the intensity of daily rainfall in Ethiopia especially,when these distributions are combined with MCAYM.展开更多
基金Supported by the National Basic Research Program of China （2009CB219603, 2010CB226800） the National Natural Science Foundation of China （40874071, 40672104）
文摘During the process of coal prospecting and exploration, different measurement time, different logging instruments and series can lead to systematic errors in well logs. Accordingly, all logging curves need to be normalized in the mining area. By studying well-logging normalization methods, and focusing on the characteristics of the coalfield, the frequency histogram method was used in accordance with the condition of the Guqiao Coal Mine. In this way, the density and sonic velocity at marker bed in the non-key well were made to close to those in the key well, and were eventually equal. Well log normalization was completed when this method was applied to the entire logging curves. The results show that the scales of logging data were unified by normalizing coal logging curves, and the logging data were consistent with wave impedance inversion data. A satisfactory inversion effect was obtained.
基金Supported by the Gastric Cancer Laboratory and Pathology Department of Chinese Medical University,Shenyang,Chinathe Science and Technology Program of Shenyang,No. 1081232-1-00
文摘AIM:To investigate the efficiency of Cox proportional hazard model in detecting prognostic factors for gastric cancer.METHODS:We used the log-normal regression model to evaluate prognostic factors in gastric cancer and compared it with the Cox model.Three thousand and eighteen gastric cancer patients who received a gastrectomy between 1980 and 2004 were retrospectively evaluated.Clinic-pathological factors were included in a log-normal model as well as Cox model.The akaike information criterion (AIC) was employed to compare the efficiency of both models.Univariate analysis indicated that age at diagnosis,past history,cancer location,distant metastasis status,surgical curative degree,combined other organ resection,Borrmann type,Lauren's classification,pT stage,total dissected nodes and pN stage were prognostic factors in both log-normal and Cox models.RESULTS:In the final multivariate model,age at diagnosis,past history,surgical curative degree,Borrmann type,Lauren's classification,pT stage,and pN stage were significant prognostic factors in both log-normal and Cox models.However,cancer location,distant metastasis status,and histology types were found to be significant prognostic factors in log-normal results alone.According to AIC,the log-normal model performed better than the Cox proportional hazard model (AIC value:2534.72 vs 1693.56).CONCLUSION:It is suggested that the log-normal regression model can be a useful statistical model to evaluate prognostic factors instead of the Cox proportional hazard model.
文摘Voltage scaling has been extensively used in industry for decades to reduce power consumption.In recent years,exploring digital circuit operation in moderate inversion has created an interest among researchers due to its immense capability to provide a perfect tradeoff between high performance and low energy operation.But circuits operating in moderate inversion are susceptible to process variations and variability.To compute variability,statistical parameters such as the probability density function(PDF)and cumulative distribution function(CDF)are required.This paper presents an analytical model framework for delay calculations utilizing log skew normal distribution for ultradeep submicron technology nodes up to 22 nm.The CDF of the proposed model is utilized to calculate minimum and maximum delays with 3σ-accuracy providing better accuracy than the conventional methods.The obtained results are also compared with Monte Carlo simulations with errors lying within the acceptable range of 2%-4%.
基金theStateNatureScienceFoundation PRC (No :5 860 170 )
文摘A large number of particulate size distributions of welding aerosols are measured by means of DMPS method, several distribution types are presented. Among them the single peak distribution is the basic composing unit of particulate size. The research on the mathematic models and distributions functions shows that the single peak distribution features the log normal distribution. The diagram estimating method (DEM) is a concise approach to dealing with distribution types, obtaining distribution functions for the particulate sizes of welding aerosols. It proves that the distribution function of particulate size possesses the extending property, being from quantity distribution to volume, as well as high order moment distributions, with K S method verifying the application of single peak distribution and of DEM.
文摘This paper aims at the spatiotemporal distribution of rainfall in Ethiopia and developing stochastic daily rainfall model.Particularly,in this study,we used a Markov Chain Analogue Year(MCAY)model that is,Markov Chain with Analogue year(AY)component is used to model the occurrence process of daily rainfall and the intensity or amount of rainfall on wet days is described using Weibull,Log normal,mixed exponential and Gamma distributions.The MCAY model best describes the occurrence process of daily rainfall,this is due to the AY component included in the MC to model the frequency of daily rainfall.Then,by combining the occurrence process model and amount process model,we developed Markov Chain Analogue Year Weibull model(MCAYWBM),Markov Chain Analogue Year Log normal model(MCAYLNM),Markov Chain Analogue Year mixed exponential model(MCAYMEM)and Markov Chain Analogue Year gamma model(MCAYGM).The performance of the models is assessed by taking daily rainfall data from 21 weather stations(ranging from 1 January 1984–31 December 2018).The data is obtained from Ethiopia National Meteorology Agency(ENMA).The result shows that MCAYWBM,MCAYMEM and MCAYGM performs very well in the simulation of daily rainfall process in Ethiopia and their performances are nearly the same with a slight difference between them compared to MCAYLNM.The mean absolute percentage error(MAPE)in the four models:MCAYGM,MCAYWBM,MAYMEM and MCAYLNM are 2.16%,2.27%,2.25%and 11.41%respectively.Hence,MCAYGM,MCAYWBM,MAYMEM models have shown an excellent performance compared to MCAYLNM.In general,the light tailed distributions:Weibull,gamma and mixed exponential distributions are appropriate probability distributions to model the intensity of daily rainfall in Ethiopia especially,when these distributions are combined with MCAYM.
