With determination micro-Fe by 1, 10-phenanthroline spectrophotometry for example, they are systematically introduced the combinatorial measurement and regression analysis method application about metheodic principle,...With determination micro-Fe by 1, 10-phenanthroline spectrophotometry for example, they are systematically introduced the combinatorial measurement and regression analysis method application about metheodic principle, operation step and data processing in the instrumental analysis, including: calibration curve best linear equation is set up, measurand best linear equation is set up, and calculation of best value of a concentration. The results showed that mean of thrice determination , s = 0 μg/mL, RSD = 0. Results of preliminary application are simply introduced in the basic instrumental analysis for atomic absorption spectrophotometry, ion-selective electrodes, coulometry and polarographic analysis and are contrasted to results of normal measurements.展开更多
The twists of twelve kinds of the cotton OE yarns in different machine twists and different yarn linear density have been determined by one time back twisting and twisting method, triple twisting method and double twi...The twists of twelve kinds of the cotton OE yarns in different machine twists and different yarn linear density have been determined by one time back twisting and twisting method, triple twisting method and double twisting method. The quantitative analysis of the measurement principles of all the untwisting and retwisting methods has been made by the writers. It is theoretically and practically concluded that for twelve kinds of the cotton OE yarns the difference between the twist determined by triple twisting method and machine twist is the lowest and there is measuring method error in determining the twist of OE yarn by all the untwisting and retwisting methods.展开更多
Aims Accurate forecast of ecosystem states is critical for improving natural resourcemanagement and climate change mitigation.Assimilating observed data into models is an effective way to reduce uncertainties in ecolo...Aims Accurate forecast of ecosystem states is critical for improving natural resourcemanagement and climate change mitigation.Assimilating observed data into models is an effective way to reduce uncertainties in ecological forecasting.However,influences ofmeasurement errors on parameter estimation and forecasted state changes have not been carefully examined.This study analyzed the parameter identifiability of a process-based ecosystem carbon cycle model,the sensitivity of parameter estimates and model forecasts to the magnitudes of measurement errors and the information contributions of the assimilated data to model forecasts with a data assimilation approach.Methods We applied a Markov Chain Monte Carlo method to assimilate eight biometric data sets into the Terrestrial ECOsystemmodel.The data were the observations of foliage biomass,wood biomass,fine root biomass,microbial biomass,litter fall,litter,soil carbon and soil respiration,collected at the Duke Forest free-air CO_(2)enrichment facilities from 1996 to 2005.Three levels ofmeasurement errorswere assigned to these data sets by halving and doubling their original standard deviations.Important Findings Results showed that only less than half of the 30 parameters could be constrained,though the observations were extensive and themodelwas relatively simple.Highermeasurement errors led to higher uncertainties in parameters estimates and forecasted carbon(C)pool sizes.The longterm predictions of the slow turnover pools were affected less by the measurement errors than those of fast turnover pools.Assimilated data contributed less information for the pools with long residence times in long-term forecasts.These results indicate the residence times of C pools played a key role in regulating propagation of errors from measurements to model forecasts in a data assimilation system.Improving the estimation of parameters of slowturnover C pools is the key to better forecast long-term ecosystem C dynamics.展开更多
文摘With determination micro-Fe by 1, 10-phenanthroline spectrophotometry for example, they are systematically introduced the combinatorial measurement and regression analysis method application about metheodic principle, operation step and data processing in the instrumental analysis, including: calibration curve best linear equation is set up, measurand best linear equation is set up, and calculation of best value of a concentration. The results showed that mean of thrice determination , s = 0 μg/mL, RSD = 0. Results of preliminary application are simply introduced in the basic instrumental analysis for atomic absorption spectrophotometry, ion-selective electrodes, coulometry and polarographic analysis and are contrasted to results of normal measurements.
文摘The twists of twelve kinds of the cotton OE yarns in different machine twists and different yarn linear density have been determined by one time back twisting and twisting method, triple twisting method and double twisting method. The quantitative analysis of the measurement principles of all the untwisting and retwisting methods has been made by the writers. It is theoretically and practically concluded that for twelve kinds of the cotton OE yarns the difference between the twist determined by triple twisting method and machine twist is the lowest and there is measuring method error in determining the twist of OE yarn by all the untwisting and retwisting methods.
基金This research was financially supported by the Office of Science(BER),Department of Energy(DE-FG02-006ER64319)through the Midwestern Regional Center of the National Institute for Climatic Change Research at Michigan Technological University,under Award Number DE-FC02-06ER64158by National Science Foundation(DEB0078325 andDEB0743778).Themodel runswere performed at the Supercomputing Center for Education&Research(OSCER),University of Oklahoma.
文摘Aims Accurate forecast of ecosystem states is critical for improving natural resourcemanagement and climate change mitigation.Assimilating observed data into models is an effective way to reduce uncertainties in ecological forecasting.However,influences ofmeasurement errors on parameter estimation and forecasted state changes have not been carefully examined.This study analyzed the parameter identifiability of a process-based ecosystem carbon cycle model,the sensitivity of parameter estimates and model forecasts to the magnitudes of measurement errors and the information contributions of the assimilated data to model forecasts with a data assimilation approach.Methods We applied a Markov Chain Monte Carlo method to assimilate eight biometric data sets into the Terrestrial ECOsystemmodel.The data were the observations of foliage biomass,wood biomass,fine root biomass,microbial biomass,litter fall,litter,soil carbon and soil respiration,collected at the Duke Forest free-air CO_(2)enrichment facilities from 1996 to 2005.Three levels ofmeasurement errorswere assigned to these data sets by halving and doubling their original standard deviations.Important Findings Results showed that only less than half of the 30 parameters could be constrained,though the observations were extensive and themodelwas relatively simple.Highermeasurement errors led to higher uncertainties in parameters estimates and forecasted carbon(C)pool sizes.The longterm predictions of the slow turnover pools were affected less by the measurement errors than those of fast turnover pools.Assimilated data contributed less information for the pools with long residence times in long-term forecasts.These results indicate the residence times of C pools played a key role in regulating propagation of errors from measurements to model forecasts in a data assimilation system.Improving the estimation of parameters of slowturnover C pools is the key to better forecast long-term ecosystem C dynamics.
