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Calibration is an operation whose main objective is to know the metrological status of a measurement system. Nevertheless, in analytical sciences, calibration has special connotations since it is the basis to do the quantification...
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Calibration is an operation whose main objective is to know the metrological status of a measurement system. Nevertheless, in analytical sciences, calibration has special connotations since it is the basis to do the quantification of the amount of one or more components (analytes) in a sample, or to obtain the value of one or more analytical parameters related with that quantity. Regarding this subject, the aim of analytical calibration is to find an empiric relationship, called measurement function, which permits subsequently to calculate the values of the amount (x-variable) of a substance in a sample, from the measured values on it of an analytical signal (y-variable). In this paper, the metrological bases of analytical calibration and quantification are established and, the different work schemes and calibration methodologies, which can be applied depending on the characteristic of the sample (analyte + matrix) to analyse, are distinguished and discussed. Likewise, the different terms and related names are clarified. A special attention has been paid to those analytical methods which use separation techniques, in relation with its effect on calibration operations and later analytical quantification.
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MOTIVATION: Reproducibility analyses of biologically relevant microarray studies have mostly focused on overlap of detected biomarkers or correlation of differential expression evidences across studies. For clinical utility, direc...
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MOTIVATION: Reproducibility analyses of biologically relevant microarray studies have mostly focused on overlap of detected biomarkers or correlation of differential expression evidences across studies. For clinical utility, direct inter-study prediction (i.e. to establish a prediction model in one study and apply to another) for disease diagnosis or prognosis prediction is more important. Normalization plays a key role for such a task. Traditionally, sample-wise normalization has been a standard for inter-array and inter-study normalization. For gene-wise normalization, it has been implemented for intra-study or inter-study predictions in a few papers while its rationale, strategy and effect remain unexplored. RESULTS: In this article, we investigate the effect of gene-wise normalization in microarray inter-study prediction. Gene-specific intensity discrepancies across studies are commonly found even after proper sample-wise normalization. We explore the rationale and necessity of gene-wise normalization. We also show that the ratio of sample sizes in normal versus diseased groups can greatly affect the performance of gene-wise normalization and an analytical method is developed to adjust for the imbalanced ratio effect. Both simulation results and applications to three lung cancer and two prostate cancer data sets, considering both binary classification and survival risk predictions, showed significant and robust improvement of the new adjustment. A calibration scheme is developed to apply the ratio-adjusted gene-wise normalization for prospective clinical trials. The number of calibration samples needed is estimated from existing studies and suggested for future applications. The result has important implication to the translational research of microarray as a practical disease diagnosis and prognosis prediction tool.
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We report an examination of the UK stack testing industry's proficiency for monitoring industrial emissions of SO_2, NO and particulates from 2000 to 2011. Data were taken from three proficiency testing schemes run by the National...
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We report an examination of the UK stack testing industry's proficiency for monitoring industrial emissions of SO_2, NO and particulates from 2000 to 2011. Data were taken from three proficiency testing schemes run by the National Physical Laboratory (NPL), UK; Calibration Gas Scheme (gas bottle certified reference materials), Gas Measurement Scheme (using a Stack Simulator Facility to test the entire measurement system) and Particulate Scheme (foil shims and salt solutions--i.e., filter and probe washing simulants). In each round of each scheme, participants' deviations from assigned value were normalised to an allowable deviation based on the required uncertainty for stack emission measurements stipulated in the European Union's Industrial Emissions Directive. This normalisation produced a z-score and limits were set to define satisfactory, warning and unsatisfactory participant performance. As a function of time, it was found that across all schemes, the number of unsatisfactory/outlier scores decreased, evidencing an overall improvement in industry proficiency. With regard to the gas schemes, it was found that the industry had a poorer proficiency for SO_2 than NO and that there was a distribution bias toward negative scores in the Gas Measurement scheme consistent with SO_2 sample losses in drying units. It was evident that this industry bias was insufficient to force the vast majority of the industry outside of the satisfactory z-score limits; however, it was noted that this issue should be carefully monitored in the future.
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We report an examination of the UK stack testing industry’s proficiency for monitoring industrial emissions of SO2, NO and particulates from 2000 to 2011. Data were taken from three proficiency testing schemes run by the National...
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We report an examination of the UK stack testing industry’s proficiency for monitoring industrial emissions of SO2, NO and particulates from 2000 to 2011. Data were taken from three proficiency testing schemes run by the National Physical Laboratory (NPL), UK; Calibration Gas Scheme (gas bottle certified reference materials), Gas Measurement Scheme (using a Stack Simulator Facility to test the entire measurement system) and Particulate Scheme (foil shims and salt solutions—i.e., filter and probe washing simulants). In each round of each scheme, participants’ deviations from assigned value were normalised to an allowable deviation based on the required uncertainty for stack emission measurements stipulated in the European Union’s Industrial Emissions Directive. This normalisation produced a z-score and limits were set to define satisfactory, warning and unsatisfactory participant performance. As a function of time, it was found that across all schemes, the number of unsatisfactory/outlier scores decreased, evidencing an overall improvement in industry proficiency. With regard to the gas schemes, it was found that the industry had a poorer proficiency for SO2 than NO and that there was a distribution bias toward negative scores in the Gas Measurement scheme consistent with SO2 sample losses in drying units. It was evident that this industry bias was insufficient to force the vast majority of the industry outside of the satisfactory z-score limits; however, it was noted that this issue should be carefully monitored in the future.
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Automatic calibration of complex hydro-ecological models is an increasingly important issue which involves making decisions. One of the most relevant is the choice of the objective function, but its effects have been scarcely stud...
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Automatic calibration of complex hydro-ecological models is an increasingly important issue which involves making decisions. One of the most relevant is the choice of the objective function, but its effects have been scarcely studied in complex models. We have used the SWAT model to assess the impact of the objective function for a multi-site (4 stations) and multi-variable (OrgP, OrgN, NO3-, PO43-) calibration of the Odense catchment (Denmark). Six calibration schemes were tested, varying the objective function and the nutrient fractions targeted. The best performance metrics (R2, NSE, PBIAS) were obtained when using NSE as objective function and targeting N-fractions and P-fractions separately. The scheme was validated in another SWAT set-up in northern Denmark. Although NSE is often questioned, we found it as an adequate objective function when addressing a multi-site and multi-variable calibration. Our findings may serve as guideline for hydro-ecological modellers, being useful to achieve watershed management goals. (C) 2017 Elsevier Ltd. All rights reserved.
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Calibration of inertial measurement units (IMU) is carried out to estimate the coefficients which transform the raw outputs of inertial sensors to meaningful quantities of interest. Based on the fact that the norms of the measured...
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Calibration of inertial measurement units (IMU) is carried out to estimate the coefficients which transform the raw outputs of inertial sensors to meaningful quantities of interest. Based on the fact that the norms of the measured outputs of the accelerometer and gyroscope cluster are equal to the magnitudes of specific force and rotational velocity inputs, respectively, an improved multi-position calibration approach is proposed. Specifically, two open but important issues are addressed for the multi-position calibration: (1) calibration of inter-triad misalignment between the gyroscope and accelerometer triads and (2) the optimal calibration scheme design. A new approach to calibrate the inter-triad misalignment is devised using the rotational axis direction measurements separately derived from the gyroscope and accelerometer triads. By maximizing the sensitivity of the norm of the IMU measurement with respect to the calibration parameters, we propose an approximately optimal calibration scheme. Simulations and real tests show that the improved multi-position approach outperforms the traditional laboratory calibration method, meanwhile relaxing the requirement of precise orientation control.
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Carbon exchange between the atmosphere and terrestrial ecosystem is a key component affecting climate changes. Because the in situ measurements are not dense enough to resolve CO2 exchange spatial variation on various scales, the ...
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Carbon exchange between the atmosphere and terrestrial ecosystem is a key component affecting climate changes. Because the in situ measurements are not dense enough to resolve CO2 exchange spatial variation on various scales, the variation has been mainly simulated by numerical ecosystem models. These models contain large uncertainties in estimating CO2 exchange owing to incorporating a number of empirical parameters on different scales. This study applied a global optimization algorithm and ensemble approach to a surface CO2 flux scheme to (1) identify sensitive photosynthetic and respirational parameters, and (2) optimize the sensitive parameters in the modeling sense and improve the model skills. The photosynthetic and respirational parameters of corn (C4 species) and soybean (C3 species) in NCAR land surface model (LSM) are calibrated against observations from AmeriFlux site at Bondville, IL during 1999 and 2000 growing seasons. Results showed that the most sensitive parameters are maximum carboxylation rate at 25°C and its temperature sensitivity parameter (V cmax25 and a vc ), quantum efficiency at 25°C (Q e25), temperature sensitivity parameter for maintenance respiration (a rm ), and temperature sensitivity parameter for microbial respiration (a mr ). After adopting calibrated parameter values, simulated seasonal averaged CO2 fluxes were improved for both the C4 and the C3 crops (relative bias reduced from 0.09 to 0.02 for the C4 case and from 0.28 to -0.01 for the C3 case). An updated scheme incorporating new parameters and a revised flux-integration treatment is also proposed.
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Parameter calibration is an importantly preliminary step before using a crop model to simulate crop growth and final yield. Compared with the traditionally accepted calibration method parameterizing the whole model simultaneously ...
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Parameter calibration is an importantly preliminary step before using a crop model to simulate crop growth and final yield. Compared with the traditionally accepted calibration method parameterizing the whole model simultaneously (called as "Global Scheme"), the Sub-Model Component (SMC) Scheme emphasizes on parameterizing different functional modules in a crop model sequentially. However, the SMC Scheme receives less attention, especially at regional scales. Therefore, this study led a performance evaluation of the two calibration schemes through using them to incorporate remote sensing data into a crop model (MCWLA-Rice) independently in Northeast China. We found the SMC Scheme reduced root mean square error (RMSE) on average by 4 days for heading date and 2 days for harvest date. Using the Pearson correlation coefficient (R) to assess the similarity between time series of modelled LAI and remotely-sensed LAI, the SMC Scheme decreased LAI estimation error by 0.04. Finally, the SMC Scheme greatly decreased relative RMSE (RRMSE) for yield by 11%. In addition, temperature and topography could affect the performance of SMC Scheme. Our findings demonstrated that the SMC Scheme calibrated the crop model more effectively and reliably, suggesting its potentially wide application in other regions and crops.
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We solve the problem of coupled heat conduction and water transport in bentonite with water present in vapour and adsorbed form in a non-equilibrium state. The problem is governed by a system of two parabolic PDE and one ODE. Most...
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We solve the problem of coupled heat conduction and water transport in bentonite with water present in vapour and adsorbed form in a non-equilibrium state. The problem is governed by a system of two parabolic PDE and one ODE. Most of the coefficients are non-linear functions, defined either by underlying physical phenomena or empirically. We present a numerical scheme using FEM with linear base functions, implicit time discretization, and simple iterations for the non-linear terms. The model is verified against experiments (one 1D and one 3D) and we demonstrate the use of optimization algorithm for parameter calibration. Some of the parameters could be estimated successfully while others with limited confidence, which is explained by the particular character of the non-linear parameter dependence and resulting small sensitivity of the model on some parameters.
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In this article, we deal with calibration and Monte Carlo simulation of the Wishart stochastic volatility model. Despite the analytical tractability of the considered model, being of affine type, the implementation of Wishart-base...
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In this article, we deal with calibration and Monte Carlo simulation of the Wishart stochastic volatility model. Despite the analytical tractability of the considered model, being of affine type, the implementation of Wishart-based stochastic volatility models poses non-trivial challenges from a numerical point of view. The goal of this article is to overcome these problems providing efficient numerical schemes for Monte Carlo simulations. Moreover, a fast and accurate calibration procedure is proposed.
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