Uncertainty Evaluation Research Articles

Quantification of uncertainties in reference and relative dose measurements, dose calculations, and patient setup in modern external beam radiotherapy.

Uncertainties in the steps of external beam radiotherapy (EBRT) affect patient outcomes. However, few studies have investigated major contributors to these uncertainties. This study investigated factors contributing to reducing uncertainty in delivering a dose to a target volume. The EBRT process was classified into four steps: reference dosimetry, relative dosimetry [percentage depth doses (PDDs) and off-center ratios (OCRs)], dose calculations (PDDs and OCRs in a virtual water phantom), and patient setup using an image-guided radiation therapy system. We evaluated the uncertainties for these steps in conventionally fractionated EBRT for intracranial disease using 4-, 6-, and 10-MV flattened photon beams generated from clinical linear accelerators following the Guide to the Expression of Uncertainty in Measurement and an uncertainty evaluation method with uncorrected deflection. The following were the major contributors to these uncertainties: beam quality conversion factors for reference dosimetry; charge measurements, chamber depth, source-to-surface distance, water evaporation, and field size for relative dosimetry; dose calculation accuracy for the dose calculations; image registration, radiation-imaging isocenter coincidence, variation in radiation isocenter due to gantry and couch rotation, and intrafractional motion for the patient setup. Among the four steps, the relative dosimetry and dose calculation (namely, both penumbral OCRs) steps involved an uncertainty of more than 5% with a coverage factor of 1. In the EBRT process evaluated herein, the uncertainties in the relative dosimetry and dose calculations must be reduced.

Study on measurement uncertainty of energy conversion efficiency of tidal energy converters

Accurate assessment of the measurement uncertainty in the energy conversion efficiency of tidal energy converters is pivotal for a comprehensive analysis of experimental results. This paper presents a sophisticated measurement uncertainty assessment model which is based on energy conversion efficiency experiments of tidal energy converters. The model conducts a careful assessment of the input quantities using both Type A and Type B standard measurement uncertainties and provides an extended understanding of the experimental result uncertainties. The findings reveal that: (1) Even with human factors excluded, the experimental results show a dispersion ranging from 1.5 % to 4.2 % under the optimal experimental conditions; (2) Compared with the Type B standard measurement uncertainty evaluation method, the experimental results based on the Type A standard measurement uncertainty evaluation are more discrete, but the experimental results are more realistic; (3) Through the data analysis of the experimental results, the flow rate becomes a primary factor influencing the measurement uncertainty of experimental results. This paper provides a theoretical reference for scientific analysis of energy capture efficiency of tidal energy converters and for improving the credibility of experimental results.

Purity determination and uncertainty evaluation of L-selenium-methylselenocysteine

In our country, sodium selenite and sodium selenate are still used as nutritional fortifiers of selenium. Both of them are inorganic selenium, the absorption effect of human body is lower, the risk of toxic wind is larger, and it is easy to cause selenium poisoning. L-selenium-methylselenocysteine (L-SeMC) is a good source of organic selenium, which has higher biological activity and availability. At present, L-SeMC solid standard materials have not yet been developed, a high accuracy and low technical requirement method is needed to determine the purity of candidate reference materials (RMs). In this way, the key steps in the development of reference materials can be completed, and provide a source of high quality selenium supplement for people. In this paper, the candidate structures were identified and analyzed by high performance liquid chromatography (HPLC), Fourier transform infrared (FTIR), and nuclear magnetic resonance (NMR). Homogeneity, together with stability were also measured by HPLC. The sample can be transported for a short period of one month under normal conditions, which can also be stored for a long time within the temperature range of −20 °C to room temperature for more than one year. The purity of L-SeMC measured by mass balance method and quantitative nuclear magnetic resonance (qNMR) was 99.5 % with an expanded uncertainty of 0.4 % (k = 2). However, differential scanning calorimetry (DSC) was not suitable for the purity determination of L-SeMC samples. This method has a wide range of applications and may provide a reference for the purity determination of other RMs.

17O Destruction Rate in Stars

In recent years, several laboratory studies of CNO cycle-related nuclear reactions have been carried out. Nevertheless, extant models of stellar nucleosynthesis still adopt CNO reaction rates reported in old compilations, such as NACRE or CF88. In order to update these rates, we performed new calculations based on a Monte Carlo R-Matrix analysis. In more detail, a method was developed that is based on the collection of all the available data, including recent low-energy measurements obtained by the LUNA collaboration in the reduced background environment of the INFN-LNGS underground laboratory, on R-Matrix cross-section calculations with the AZURE2 code and on uncertainty evaluations with a Monte Carlo analysis. As a first scientific benchmark case, the reactions 17O(p,γ)18F and 17O(p,α)14N were investigated. Among the different stellar scenarios they can influence, the 16O/17O abundance ratio in RGB and AGB stars is the one that can be directly confirmed from spectroscopic measurements. The aim is to reduce the nuclear physics uncertainties, thus providing a useful tool to constrain deep mixing processes eventually taking place in these stars. In this work, we present the procedure we followed to calculate the 17O(p,γ)18F and the 17O(p,α)14N reaction stellar rates and preliminary comparisons with similar rates reported in widely used nuclear physics libraries are discussed.

USING RETROSPECTIVE DATA TO ASSESS THE UNCERTAINTY OF INDICATORS OF BIOLOGICAL SAFETY OF FOOD AND FEED RAW MATERIALS

The results of the analysis of the advantages and disadvantages of the use of retrospective data for the implementation of the evaluation of uncertainty in the measurement of indicators of biological safety of food and feed raw materials are given. The model measurement procedure used quantitative determination of genetically modified organisms by means of polymerase chain reaction in real time 250 measurement results of certified reference material with a content of 0.1% genetically modified soybean line obtained under conditions of intra-laboratory reproducibility and 10 results of relevant inter-laboratory comparisons with the participation accredited provider. The suitability of retrospective data was assessed by the Shapiro-Wilk criterion and statistical controllability analysis using standardized score charts and modified Shewhart charts, uncertainty by 4 methods described in general and branch international guidelines and regulations. The results of using a combination of modified Shewhart maps demonstrate the acceptability of this approach for establishing the suitability of retrospective measurement results as input data for uncertainty assessment. By integrating additional "fitness limits" to a set of standardized control limits, this analysis acquires the ability to simultaneously establish not only stability, through the interpretation of geometric patterns, but also compliance with specific criteria. Using this approach, objective evidence of the suitability of retrospective data for estimating measurement uncertainty was obtained. The distribution of retrospective data obtained under conditions of intralaboratory reproducibility is assumed to be normal. As a result of the assessment of statistical controllability and compliance with the established criteria, objective evidence of the suitability of the data for uncertainty assessment was obtained. Analysis of uncertainty intervals of subgroups with the volume of 25 measurements, using 4 assessment methods, demonstrated significant variability in the results of measuring biological safety indicators. The uncertainty interval could narrow or widen by almost 100% of the value within fifty measurements. Median values obtained using different uncertainty estimation methods were 2.69%, 22.69%, 39.23, and 43.08%. This variability is due to the limited coverage of sources of uncertainty, which is inherent in each of the analyzed methods. The most optimal method is the evaluation based on the routine measurement of the certified reference material. Retrospective results of routine measurement of certified reference material and interlaboratory comparisons are an effective source of input data for assessing the uncertainty of the results of measuring biological safety indicators of agricultural raw materials. There is a need for further research to establish an assessment model that will provide an opportunity to take into account the optimal number of influential sources of uncertainty that are inherent in biological systems and cannot be simultaneously taken into account using existing approaches offered by standard assessment methods.

Uncertainty Evaluation for Autonomous Vehicles: A Case Study of AEB System

Uncertainty Evaluation for Autonomous Vehicles: A Case Study of AEB System

Uncertainty Evaluation and Compensation for Reservoir’s Bathymetric Patterns Predicted with Radial Basis Function Approaches Based on Conventionally Acquired Water Depth Data

Uncertainty Evaluation and Compensation for Reservoir’s Bathymetric Patterns Predicted with Radial Basis Function Approaches Based on Conventionally Acquired Water Depth Data

Novel analysis of alignment error on spherical Fizeau interferometer and uncertainty evaluation of sphericity calibration system based on random ball test

The National Metrology Institute of Japan (NMIJ) has developed a high-precision sphericity calibration system utilizing a Fizeau interferometer and conducted a thorough evaluation of the system's measurement uncertainty. Generally, there are two principal sources of uncertainty in the measurement outcomes when utilizing a spherical Fizeau interferometer. First, the accuracy of the system is significantly influenced by the prior knowledge of the reference sphere surface's absolute profile. To address this, the study established a straightforward yet effective calibration system for the reference sphere surface using a random ball test method. Second, the system's precision is affected by misalignment aberration, which occurs when there is any lateral or longitudinal displacement of the test sphere from the confocal position relative to the reference sphere. This misalignment can introduce both high-order shape errors (misalignment aberrations) and low-order shape errors (alignment errors). Through analytical consideration of an observation coordinate system on the camera plane, this study delves into misalignment aberrations, suggesting that the impact of misalignment should be determined experimentally for each reference sphere unit due to potential imperfections that may be revealed by misalignment. Furthermore, the study proposes that uncertainties related to misalignment aberrations are theoretically confirmed to be smaller than previous studies by conducting an in-depth uncertainty analysis of the misalignment, with a focus on the observation coordinate system on the camera plane. Notably, the research demonstrates that a measurement uncertainty level of λ/100 is achievable, maintaining a broader tolerance for misalignment than previously reported studies. The uncertainty of calibration of the reference sphere unit's absolute profile was 4.2 nm and the uncertainty of sample measurement was determined to be 4.6 nm with misalignment tolerance of ±λ/10. This advancement marks a stride toward improving the accuracy and reliability of sphericity measurements, offering potential for widespread application in precision engineering and metrology.

Use of Deep-Learning-Accelerated Gradient Approximation for Reservoir Geological Parameter Estimation

The estimation of space-varying geological parameters is often not computationally affordable for high-dimensional subsurface reservoir modeling systems. The adjoint method is generally regarded as an efficient approach for obtaining analytical gradient and, thus, proceeding with the gradient-based iteration algorithm; however, the infeasible memory requirement and computational demands strictly prohibit its generic implementation, especially for high-dimensional problems. The autoregressive neural network (aNN) model, as a nonlinear surrogate approximation, has gradually received increasing popularity due to significant reduction of computational cost, but one prominent limitation is that the generic application of aNN to large-scale reservoir models inevitably poses challenges in the training procedure, which remains unresolved. To address this issue, model-order reduction could be a promising strategy, which enables us to train the neural network in a very efficient manner. A very popular projection-based linear reduction method, i.e., propel orthogonal decomposition (POD), is adopted to achieve dimensionality reduction. This paper presents an architecture of a projection-based autoregressive neural network that efficiently derives an easy-to-use adjoint model by the use of an auto-differentiation module inside the popular deep learning frameworks. This hybrid neural network proxy, referred to as POD-aNN, is capable of speeding up derivation of reduced-order adjoint models. The performance of POD-aNN is validated through a synthetic 2D subsurface transport model. The use of POD-aNN significantly reduces the computation cost while the accuracy remains. In addition, our proposed POD-aNN can easily obtain multiple posterior realizations for uncertainty evaluation. The developed POD-aNN emulator is a data-driven approach for reduced-order modeling of nonlinear dynamic systems and, thus, should be a very efficient modeling tool to address many engineering applications related to intensive simulation-based optimization.

Inhibition sensitivity of invitro firefly bioluminescence quantum yields to Zn2+ and Cd2+ concentrations in aqueous solutions.

To elucidate the inhibition effects of Zn2+ and Cd2+ on the luciferin-luciferase reaction, we performed quantitative measurements of quantum yields and spectral shapes for invitro firefly bioluminescence in aqueous solutions containing ZnSO4, ZnCl2, CdSO4, and CdCl2 at different concentrations. Particular care was taken toward the equilibrium between metal ions and enzyme proteins, anion difference, solubility, and uncertainty evaluation. The bioluminescence quantum yields decreased almost linearly to the concentration of Zn2+ and Cd2+ below 0.25 mM. No obvious difference was found between the chloride and sulfate anion solutions. We defined inhibition sensitivity as the decrease in relative quantum yield versus the concentration of metal ions, and they were determined to be 1.48 ± 0.13 and 1.13 ± 0.16/mM for Zn2+ and Cd2+, respectively. We estimated the detection limit of inhibition effects as the concentration of metal ions that decrease relative quantum yields by 10%, which were 0.07 mM (4 ppm) and 0.09 mM (10 ppm) for Zn2+ and Cd2+, respectively. The shape of the bioluminescence spectra changed sensitively with the increase in Zn2+ concentrations. The bioluminescence peak energy for 0.10-mM Zn2+ was ~2.2 eV, while that for 0.25-mM Zn2+ was ~2.0 eV. The shape of the spectra changed less sensitively with the increase in Cd2+concentrations, and the peak energy was at ~2.2 eV for Cd2+ concentrations of 0.10 and 0.25 mM.

Bhatia-Davis inequalities for lower and upper previsions and covariances

We explore how the classical Bhatia-Davis inequality bounding variances can be extended to uncertainty evaluations of gambles (bounded random numbers) by means of imprecise (lower or upper) previsions with different degrees of consistency. Firstly, a number of extensions are found with 2-coherent imprecise previsions. Subsequently, bounds with coherent lower and upper previsions are investigated, together with applications bounding lower and upper variances as well as p-boxes. Finally, bounds for covariances and for lower and upper covariances are obtained. Like the classical situation, imprecise Bhatia-Davis inequalities require a reduced amount of uncertainty information to be applied. When even less information is available, we show that various versions of Popoviciu's inequality obtain.

Precision and uncertainty in natural gas calorific value estimation: advanced combinatorial predictive models for complex pipeline systems

Abstract In recent years, natural gas pipelines have been characterized by multiple intake points, the mixing of different gas sources, and significant variations in gas quality. The existing volumetric measurement system is no longer suitable for the development and operation of natural gas pipelines in China. To ensure accurate and equitable implementation of natural gas measurement, there is a gradual shift towards energy measurement. However, due to numerous measurement interfaces, installing chromatographs at all measurement stations would lead to substantial investment costs. Therefore, predicting the calorific value of natural gas is a key technology for energy measurement. In view of the issues existing in the current methods of obtaining natural gas calorific value, different prediction models of natural gas calorific value are constructed. According to the influencing factors of natural gas calorific value, the uncertainty evaluation is carried out, and the accuracy of different calorific value prediction models is analyzed by case data. The maximum error of regression equation (RE), response surface analysis (RSA), BP neural network and genetic algorithm (GA) prediction model is 2.29%, 0.43%, 0.59% and 0.45% respectively. However, the combined calorific value (CCV)prediction model is 0.41%. The results indicate that the predicted value calculated by the CCV prediction model is closer to the actual value and has higher prediction performance, which provides a new method for the calorific value prediction and measurement management of natural gas.

Consideration on the efficient calibration method for low-cost microphones

Current microphone calibration methods in the international standards are basically targeting laboratory and working standard microphones specified in the IEC 61094-2 and IEC 61094-4, respectively. These methods are promising to ensure the reliability however there are many difficulties in applying it to low-cost acoustic sensors such as MEMS microphones, which are rapidly becoming widespread, with aspects of time and cost. Moreover, since the installation conditions for these low-cost microphones are different from those of standardized measurement microphones, calibration methods that take these usage conditions into consideration are required. As potential methods to overcome the problems, the feasibility of the active coupler method applicable to surface mounted condition and the bulk calibration method in the reverberation room are investigated. Both proposed methods can be applied under actual installation conditions and enable rapid and cost-efficient calibration. In this study, methods and procedures for securing traceability are proposed, and uncertainty evaluation is conducted.

Force sensitivity calibration of artificial mastoids

The Standards and Calibration Laboratory (SCL) has set up a system for calibration of artificial mastoids to meet the growing demand for calibration of bone-conduction hearing aids and audiometers in the audiology industry of Hong Kong. An artificial mastoid (or mechanical coupler, as specified in IEC 60318-6) is a device that mimics the mechanical characteristics of the human mastoid (one of the bones of the skull behind the ear). It is basically composed of a dome-shaped structure and a force transducer. This paper mainly illustrates the method for measuring the force sensitivity level (in unit of dB re 1 pC/N) of an artificial mastoid in the frequency range of 125 Hz to 8 kHz. The calibration system has been automated to meet the demand for artificial mastoid calibration services in Hong Kong. This paper describes (i) the method of determining the force sensitivity level of an artificial mastoid, (ii) the measurement model and uncertainty evaluation, and (iii) the measurement results and details of the calibration system.

Using a Multivariate Virtual Experiment for Uncertainty Evaluation with Unknown Variance

Virtual experiments are a digital representation of a real measurement and play a crucial role in modern measurement sciences and metrology. Beyond their common usage as a modeling and validation tool, a virtual experiment may also be employed to perform a parameter sensitivity analysis or to carry out a measurement uncertainty evaluation. For the latter to be compliant with statistical principles and metrological guidelines, the procedure to obtain an estimate and a corresponding measurement uncertainty requires careful consideration. We employ a Monte Carlo sampling procedure using a virtual experiment that allows one to perform a measurement uncertainty evaluation according to the Monte Carlo approach of JCGM-101 and JCGM-102, two widely applied guidelines for uncertainty evaluation in metrology. We extend and formalize a previously published approach for simple additive models to account for a large class of non-linear virtual experiments and measurement models for multidimensionality of the data and output quantities, and for the case of unknown variance of repeated measurements. With the algorithm developed here, a simple procedure for the evaluation of measurement uncertainty is provided that may be applied in various applications that admit a certain structure for their virtual experiment. Moreover, the measurement model commonly employed for uncertainty evaluation according to JCGM-101 and JCGM-102 is not required for this algorithm, and only evaluations of the virtual experiment are performed to obtain an estimate and an associated uncertainty of the measurand. We demonstrate the efficacy of the developed approach and the effect of the underlying assumptions for a generic polynomial regression example and an example of a simplified coordinate measuring machine and its virtual representation. The results of this work highlight that considerable effort, diligence, and statistical considerations need to be invested to make use of a virtual experiment for uncertainty evaluation in a way that ensures equivalence with the accepted guidelines.

Are ESG scores driven by financial information? Evidence from European banks

In recent years, investors' increasing focus on sustainable investments and the sustainability orientation of companies has led to parallel growth in the market for environmental, social and governance (ESG) performance and ESG rating agencies. However, even though ESG rating agencies have become very influential institutions, the literature has found that ESG performance ratings provided by different agencies often differ from each other. This causes consequences that should be considered, such as complex evaluation of companies' ESG performance and uncertainty in ESG investment decisions. Therefore, it is necessary to identify which determinants influence ESG performance. This study aims to identify the internal determinants of an ESG score using bank-specific balance sheet indicators such as capital and risk ratios. The analysis focuses on the European banking sector from 2018 to 2022. Banks mainly foster the transition to a more inclusive and sustainable economy. Moreover, after the recent financial crises, banks have increased their social responsibility practices, strengthening their credibility, trust and reputation. Generalised estimating equations with standard error robust to heteroscedasticity were used. The results reveal that the factors that most influence the ESG score provided by ESG rating agencies are bank size and liquidity risk exposure. The larger the size of the bank and the lower the exposure to liquidity risk, the higher the ESG score assigned.

Uncertainty quantification based on active subspace dimensionality-reduction method for high-dimensional geometric deviations of compressors

Compressors are inevitably exposed to diverse geometric deviations from manufacturing errors and in-service degradation. Consequently, the evaluation of performance uncertainties becomes of utmost importance for compressors in engineering application. However, the presence of high-dimensional and strongly nonlinear geometric deviations poses significant challenges in efficiently and accurately assessing the performance uncertainties of compressors. This study proposes an active subspace-based dimensionality-reduction method for high-dimensional uncertainty quantification (UQ) of compressors. Based on the active subspace (AS) method, a dimensionality-reduction high-precision artificial neural network is raised to solve the dimension disaster problem for high-dimensional UQ. Additionally, a data-driven approach is used to calculate the gradient of the quantity of interest, addressing the issue of high computational cost during the AS dimensionality reduction process. Furthermore, the Shapley method is applied to explore the influence mechanism of geometric uncertainties on performance deviations of compressors. The UQ of one transonic compressor stage at design point and near stall point is conducted by the proposed method. The findings show that the original 24-dimensional uncertainties are reduced to three-dimensional uncertainties by using this method. Consequently, the required sample size is reduced by 75% while maintaining almost unchanged model accuracy. The findings reveal that the sweep and stagger deviation of the rotor are key uncertainties on the performance of the compressor. The dispersion in efficiency is attributed to variations in shock wave position and intensity, while the dispersion in total pressure ratio is primarily affected by changes in rotor work capacity. Moreover, the dispersion at near stall is 50% higher than that at design point. Therefore, when studying UQ, it is important to pay closer attention to the performance dispersion at near stall conditions.

Drifted Uncertainty Evaluation of a Compact Machine Tool Spindle Error Measurement System

The accurate measurement of spindle errors, especially quasi-static errors, is one of the key issues for the analysis and compensation of machine tool thermal errors in machining accuracy. To quantitatively analyze the influence of the measurement system’s own drift on the measurement results, a drifted uncertainty evaluation method of the precision instrument considering the time drift coefficient is proposed. This study also produced a high-precision compact spindle error measurement device (with a displacement measurement error of less than ±1.33 μm and an angular measurement error of less than ±1.42 arcsecs) as the research object to verify the proposed drift uncertainty evaluation method. A method for evaluating the drift uncertainty of the measurement system is proposed to quantitatively evaluate the system error and drift uncertainty of the measurement device. Experiments show that the drift uncertainty evaluation method proposed in this paper is more suitable for evaluating the uncertainty changes in measurement instruments during long-term measurements compared to traditional methods.

Dynamic uncertainty evaluation of cylindricity error based on Bayesian deep neural network propagation method

Most of the existing methods for measuring and evaluating product geometric tolerances are for static processes, which use lower dimensions, are more complex to calculate, and have poor traceability and dynamic manageability and control of the measurement process. In this paper, a dynamic evaluation model of cylindricity error uncertainty based on the Bayesian deep neural network propagation method is proposed. Firstly, the input random variable sampling sample containing error uncertainty is generated by proposing the improved Monte Carlo sampling method (IMCS), which has the characteristics of homogeneity and validity; then the principle and construction process of a Bayesian neural network model are introduced to generate the propagation model of multidimensional random error variables; further, the Gaussian mixture model is trained by the Monte Carlo method, and the probability density function of the target response is generated by using the Gaussian mixture model. Through numerical experiments, test analysis of bearing outer rings, and comparison with ISO standard methods, the results show that the model based on the dynamic Bayesian deep neural network propagation method can correctly and effectively assess the uncertainty dynamically, and the whole estimation process is stable.

Uncertainty Evaluation for Determination of Sibutramine in Health Food by HPLC-MS/MS

High-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was used to determine the content of sibutramine in health foods according to the BJS 201701 standard Determination of Compounds such as Sibutramine in Food. By evaluating each uncertainty component, the influencing factors of the uncertainty were identified and the expanded uncertainty of sibutramine detection in health foods was provided. Using the example of illegal addition of sibutramine to health products, the HPLC-MS/MS method was used for qualitative screening and quantitative analysis of samples. The sources of uncertainty in the quantitative results were analysed, including sample weighing, sample volume determination, standard solution preparation, standard curve fitting and determination of sample repeatability. The synthetic relative standard uncertainty of the measurement results was calculated and the expanded uncertainty was reported. By optimizing the liquid phase conditions, the sample can be detected within 5 min with a linear correlation coefficient of 0.998. When the content of sibutramine in health products was 42.2 mg/kg, the extended uncertainty of the results was 4.56 mg/kg (k = 2) with a 95% confidence interval. Among the various components affecting the results of detection, the uncertainty introduced during the process of preparing the standard solution has the greatest impact on the results. A proper evaluation of the uncertainty, detailed analysis and quantification of the various sources of uncertainty can help to improve the reliability of test results.