Quantification Of Measurement Uncertainty Research Articles

Quantifying measurement uncertainty in renal transplant biopsy assessment.

Renal transplant biopsies provide insights into graft health and support decision making. The current evidence on links between biopsy scores and transplant outcomes suggests there may be numerous factors affecting biopsy scores. Here we adopt measurement science approach to investigate the sources of uncertainty in biopsy assessment and suggest techniques to improve its robustness. Histological assessments, Remuzzi scores, biopsy processing and clinical variables are obtained from 144 repeat biopsies originating from 16 deceased-donor kidneys. We conducted sensitivity analysis to find the morphometric features with highest discriminating power and studied the dependencies of these features on biopsy and stain type. The analysis results formed a basis for recommendations on reducing the assessment variability. Most morphometric variables are influenced by the biopsy and stain types. The variables with the highest discriminatory power are sclerotic glomeruli counts, healthy glomeruli counts per unit area, percentages of interstitial fibrosis and tubular atrophy as well as diameter and lumen of the worst artery. A revised glomeruli adequacy score is proposed to improve the robustness of the glomeruli statistics, whereby a minimum of 104 µm2 of cortex tissue is recommended to keep type 1 and type 2 error probabilities below 0.15 and 0.2. The findings are transferable to several biopsy scoring systems. We hope that this work will help practitioners to understand the sources of statistical uncertainty and improve the utility of renal biopsy.

Uncertainty quantification for velocity measurement with 2D2C particle image velocimetry

Abstract In this paper, a theoretical model of dimensionless instantaneous and average velocity measurement uncertainty quantification with 2D2C particle image velocimetry (PIV) is established under the framework of generally adopted international uncertainty quantification standards. The effectiveness of the model is verified using uniform flow field testing data. Combined with semi-quantitative analysis of the theoretical model, uncertainty control suggestions for PIV measurements are given. The major sources affecting the uncertainty of instantaneous velocity measurements are the reference velocity, particle instantaneous pixel displacement, and their correlation term. For average velocity measurement uncertainty quantification, the uncertainty of particle average pixel displacement is effectively controlled by taking a large number of particle images. Thus, three single-component terms — the calibration factor, particle average pixel displacement and reference velocity — and two correlation terms — the particle average pixel displacement–calibration factor and the particle average pixel displacement–reference velocity — all make an important contribution to the average velocity measurement uncertainty. To reduce the uncertainty of PIV velocity measurement, one can reduce the reference velocity measurement uncertainty, optimize the PIV algorithm and improve the calibration factor by applying a high spatial resolution imaging system in experiments. In addition, reducing the reference velocity measurement uncertainty and improving the spatial resolution are key feasible methods.

Unbiased estimation of sampling variance for Simpson's diversity index.

Quantification of measurement uncertainty is crucial for robust scientific inference, yet accurate estimates of this uncertainty remain elusive for ecological measures of diversity. Here, we address this longstanding challenge by deriving a closed-form unbiased estimator for the sampling variance of Simpson's diversity index. In numerical tests the estimator consistently outperforms existing approaches, particularly for applications in which species richness exceeds sample size. We apply the estimator to quantify biodiversity loss in marine ecosystems and to demonstrate ligand-dependent contributions of T-cell-receptor chains to specificity, illustrating its versatility across fields. The novel estimator provides researchers with a reliable method for comparing diversity between samples, essential for quantifying biodiversity trends and making informed conservation decisions.

Uncertain damage identification methods based on residual force vector under the influence of measurement noise

This paper proposes probabilistic and interval methods, grounded in the residual force vector technique, to quantify measurement uncertainties and effectively integrate them into the identification process. In the probabilistic method, the measurement noise is assumed to be a zero-mean normal distribution, while the stiffness parameters of structural elements are assumed to follow a normal distribution, represented by mean values and standard deviations. In the interval method, the measurement noise is modelled as interval numbers, and the interval perturbation technique is employed to derive interval bounds for elemental stiffness parameters. A two-step model updating strategy is implemented, separately addressing pristine and damaged structures, to mitigate modeling errors. By using the results of probabilistic damage identification, a novel damage index termed damage expectation capable of capturing both the extent and probability of damage is introduced. Furthermore, an interval damage index is introduced to quantify the damage based on interval damage identification results. The impact of noise levels on the statistical properties of uncertain damage indices is also investigated. Numerical examples reveal that deterministic indexes exhibit a monotonic increase in average values with increasing noise levels. Particularly, as noise levels rise, the standard deviation of the combination index initially surpasses the deterministic damage index but eventually diminishes. Through the proposed methodology, structural damage can be identified even in the presence of uncertainties. The efficacy of the proposed damage identification technique is also validated by experimental data.

Influence of modeling choices and prior information on the Bayesian assessment of a reinforced concrete bridge

AbstractA lot of bridges are aging and reaching the anticipated service life. To gain insight in their remaining resistance, inspections and measurements can be performed. The resulting information and data can be used to update variables in the degradation models of these bridges. To account for spatial variation, degradation variables can be modeled with random fields. The random fields of the degradation variables are updated based on a Bayesian inference procedure, where different types of heterogenous and indirect data are accounted for. Nevertheless, in this procedure, various assumptions need to be made, such as the quantification of measurement uncertainty, the structural and degradation model to be used, the prior distributions of the variables of interest, and so forth. These assumptions can influence the results of the Bayesian inference. Hence, in this work, it will be investigated what the influence is of different assumptions and how they affect the localization and quantification of damage. This will be done by application to a reinforced concrete girder bridge. The main conclusions are that the model used in the Bayesian inference procedure should resemble the actual structure as good as possible and that simplifications that are allowed in the design can lead to posterior distributions deviating a lot from the actual situation. Moreover, it is illustrated how information from visual observations can be included in the definition of the prior distributions and how this has a beneficial effect on the posterior predictions.

Application-oriented meta-model of measurement uncertainty

This article is aimed at enhancing the meta-model of measurement, proposed by the author in 2013, in a way as to emphasise the qualitative and quantitative aspects of the assessment of measurement uncertainty. With regard to the qualitative aspect, the concept of fundamental epistemic uncertainty is interpreted from a point of view of scientific instrumentalism. With regard to the quantification of measurement uncertainty, a methodology based on comparing two mathematical models is presented and illustrated with three examples of practical importance.

Uncertainties on Climate Extreme Indices Estimated From U.S. Climate Reference Network (USCRN) Near‐Surface Temperatures

AbstractChanges in the frequency of temperature extremes are often attributed to global warming. The recent availability of near‐surface temperature data records from reference networks, such as the U.S. Climate Reference Network (USCRN), enables the quantification of measurement uncertainties. Within an activity of the Copernicus Climate Change Service, the estimation of the measurement uncertainty has been provided for USCRN temperature data, using metadata made available by the National Oceanic and Atmospheric Administration (NOAA). In this paper, four climate extreme indices (Frost Days, Summer Days, Ice Days, Tropical Nights) and the related uncertainties are calculated for the period 2006–2020 from the USCRN data set and compared with traditional indices. Moreover, the asymmetric USCRN measurement uncertainties are propagated to estimate the uncertainties of climate indices. The comparison shows expanded uncertainties homogeneously distributed with the latitude and typically within 15 days per year for Frost Days and within 10 days for Ice Days, while smaller uncertainties are estimated for Summer Days and Tropical Nights, with values typically within six to seven days per year. Positive uncertainties are typically larger than negative ones for all the indices. The values of Frost and Ice Days with the related uncertainties for USCRN have also been compared with the corresponding values calculated from reanalyses data, showing differences typically within 60 days for median values, quite often smaller than USCRN and inconsistent within the related uncertainties, Overall, the results show that USCRN measurement uncertainties increase confidence in the estimation of climate extreme indices and decisions for adaptation.

125 Quality Assured Uptake Rates for Passive Sampling of Indoor Air VOCs

Abstract Passive sampling of VOCs with Tenax® TA and thermal desorption GC-MS analysis is a simple, cost efficient and representative method to determine VOCs concentrations in indoor air. In theory, the uptake into the passive sampler is only dependent on the diffusion coefficient of the analyte in air and the geometry of the sampler (ideal adsorption). For characterization, the uptake rate (UR) is used, defined as the ratio of the uptaken mass and the exposure dose (ED - product of ambient concentration and exposure time). Various reported effective uptake rates (UR,eff) differ considerably from ideal values (UR,id). While existing compilations sort these experimental values essentially by the applicable exposure time, detailed information about the corresponding concentration range and other important influencing factors is often lacking. Moreover, the number of itemized substances is limited. To expand this data, we conducted own laboratory experiments. By quantification of measurement uncertainty and comparison of our own results to several similar laboratory studies, we want to characterize the range of applicability and level of confidence of effective uptake rates for indoor air VOCs.

Data-driven model-based flow measurement uncertainty quantification for building central cooling systems using a probabilistic approach

Uncertainties inevitably exist in measurements and may lead to biases in making management and control decisions, and thus affect the energy performance of building central cooling systems. Water flow meters are essential for the monitoring and operational control of building central cooling systems, but they often suffer from significant measurement uncertainties due to site constraints and unfavorable working environment. An effective method to quantify the flow measurement uncertainties is urgently needed. This study proposes a data-driven model-based flow measurement uncertainty quantification strategy using Bayesian inference and Markov chain Monte Carlo sampling methods. The proposed strategy is tested and validated systematically on an air-cooled chiller. Four case studies with different levels of flow measurement uncertainties are conducted. The test results show that both systematic and random uncertainties of flow measurements are quantified accurately by this strategy. The 95% Bayesian credible intervals of systematic and random uncertainties contain their pre-set (actual) values, and their posterior means (estimated values) are very close to their pre-set values. The relative errors in quantifying flow measurement uncertainties are within 10%. The performance of the proposed method is quite satisfactory. This study provides a cost-effective and promising alternative for on-site flow meter calibration in engineering practice.

A methodology to uncertainty quantification of essential ocean variables

The goal of this study is to provide a universally applicable procedure for a systematic evaluation of in situ measured data from single sensors regarding quantifying the uncertainty of the measurement results. As determining uncertainty for an environmental parameter also depends on the parameter itself, the focus here will be set on the variable water temperature in the first place. A separate analysis for salinity and other data will follow in later publications. With this first of a series of planned manuscripts on different parameters, we aim at providing a common understanding of how measurement uncertainty on single sensor measurements can be derived. Using an experimental in situ set-up with 6 different standard CTD sensors of two different brands, we created a four month-long, high-quality data set to be used to develop a reliable method for quantifying measurement uncertainties. Although the CTDs were deployed in a mooring in a coastal environment the described method can be extended to other deployment configurations as well. The described procedures have evolved as a stepwise process that takes the different perspectives of the involved authors into account, as well as the special conditions for environmental measurements, which are collected while the observed volume/area is undergoing a constant change. By sharing the ideas with other stakeholders, the basic concept can be extended to other observing programs and to other essential ocean variables.

Measuring the Prevalence of Anti-Social Behavior in Online Communities

With increasing attention to online anti-social behaviors such as personal attacks and bigotry, it is critical to have an accurate accounting of how widespread anti-social behaviors are. In this paper, we empirically measure the prevalence of anti-social behavior in one of the world's most popular online community platforms. We operationalize this goal as measuring the proportion of unmoderated comments in the 97 most popular communities on Reddit that violate eight widely accepted platform norms. To achieve this goal, we contribute a human-AI pipeline for identifying these violations and a bootstrap sampling method to quantify measurement uncertainty. We find that 6.25% (95% Confidence Interval [5.36%, 7.13%]) of all comments in 2016, and 4.28% (95% CI [2.50%, 6.26%]) in 2020, are violations of these norms. Most anti-social behaviors remain unmoderated: moderators only removed one in twenty violating comments in 2016, and one in ten violating comments in 2020. Personal attacks were the most prevalent category of norm violation; pornography and bigotry were the most likely to be moderated, while politically inflammatory comments and misogyny/vulgarity were the least likely to be moderated. This paper offers a method and set of empirical results for tracking these phenomena as both the social practices (e.g., moderation) and technical practices (e.g., design) evolve.

Study on the Impact of the Doppler Shift for CO2 Lidar Remote Sensing

Atmospheric carbon dioxide (CO2) is recognized as the most important component of the greenhouse gases, the concentration of which has increased rapidly since the pre-industrial era due to anthropogenic emissions of greenhouse gases (GHG). The accurate monitoring of carbon dioxide is essential to study the global carbon cycle and radiation budget on Earth. The Aerosol and Carbon Detection Lidar (ACDL) instrument onboard the Atmospheric Environmental Monitoring Satellite (AEMS) was successfully launched in April 2022, which allows a new perspective to quantify the global spatial distribution of atmospheric CO2 with high accuracy. In this work, the impact of the Doppler shift on CO2 measurements for an integrated-path differential absorption (IPDA) light detection and ranging (lidar) system was evaluated to meet the weighted column-averaged mixing ratio of carbon dioxide (XCO2) measurement requirements of less than one part per million (ppm). The measurement uncertainties due to the Doppler shift were first evaluated in airborne IPDA observations. The result shows that most of the Doppler shift is in the range of 6–8 MHz, resulting in 0.26-0.39 ppm deviations in the XCO2 results. The deviations between the XCO2 retrievals and in situ measurements decreased to 0.16 ppm after the correction of the Doppler shift from 11:28:29 to 11:28:49 in the flight campaign. In addition, the online Doppler shift accounts for 98% of the deviations between XCO2 retrievals and in situ measurements. Furthermore, the impact of the Doppler shift on ACDL measurements is also assessed. The differences between the XCO2 retrievals with and without Doppler shift are used to quantify measurement uncertainties due to the Doppler effect. The simulations reveal that a pointing misalignment of 0.067 mrad can lead to a mean bias of about 0.30 ppm (0.072%) in the CO2 column. In addition, CO2 measurements are more sensitive to the Doppler shift at high altitudes for IPDA lidar, so the largest differences in the CO2 columns are found on the Qinghai–Tibet Plateau in China.

Modelling and quantification of measurement uncertainty for leak localisation in a gas pipeline

To assess the accuracy of the estimated leak location along the gas pipeline, measurement uncertainty (MU) must be taken into consideration. The MU of the estimation of leak location mainly consists of five contributors: flow medium temperature, flow velocity, time difference of arrival (TDOA) of negative pressure wave (NPW), and pipeline length. A model has been proposed to determine the MU of the estimation of leak location based on the Guide to the Expression of Uncertainty in Measurement (GUM). It was found that the uncertainty propagation from measurement of dynamic pressure to the TDOA is implicit. In order to resolve this issue, Monte Carlo method is employed. The contribution of each factor to the MU of the estimation of leak location has also been analyzed. The denoising technique of Butterworth filter can minimize the uncertainty of the TDOA, which is associated with the MU of dynamic pressure sensors. The results show that the manual measurement of the length of pipeline and actual leak locations play the most crucial role in the MU of the estimation of leak location. When a leak is nearer to one extremity of the pipeline, the localization MU is influenced more greatly by the flow and temperature MUs at the other extremity of the pipeline rather than at the same end. When the leak location is moving away from the inlet of pipeline, the expanded uncertainties will increase in the range of 0.0522% to 0.0806%.

Comparing Different Uncertainty Measures to Quantify Measurement Uncertainties in High School Science Experiments

Interpreting experimental data in high school experiments can be a difficult task for students, especially when there is large variation in the data. At the same time, calculating the standard deviation poses a challenge for students. In this article, we look at alternative uncertainty measures to describe the variation in data sets. A comparison is done in terms of mathematical complexity and statistical quality. The determination of mathematical complexity is based on different mathematics curricula. The statistical quality is determined using a Monte Carlo simulation in which these uncertainty measures are compared to the standard deviation. Results indicate that an increase in complexity goes hand in hand with quality. Additionally, we propose a sequence of these uncertainty measures with increasing mathematical complexity and increasing quality. As such, this work provides a theoretical background to implement uncertainty measures suitable for different educational levels.

Laser-induced breakdown spectroscopy for local equivalence ratio measurement in opposed jet methane-air flames

Laser-induced breakdown spectroscopy (LIBS) has been applied to both non-reacting and reacting opposed jet flows to evaluate the measurement accuracy of instantaneous, local air-fuel ratio. However, the LIBS signal is much weaker in flames than in the corresponding non-reacting gases and more work is required to investigate the potential of this technique for instantaneous measurements in reacting flows and quantify measurement uncertainties. The present study reported local equivalence ratio measurements of methane/air flames in an opposed-jet burner, which allowed independent control of flow rate and air-fuel ratio. Images and emission spectra of laser-induced plasma were investigated simultaneously using both an Intensified CCD (ICCD) and a spectrometer with a high degree of spatial and temporal resolution. The influences of the camera delay time, exposure time and laser pulse energy on the LIBS measurements were investigated as well. The dependence of spectral intensity ratios of H/O and C2/CN on air-fuel ratio was quantified over a wide range of conditions extending from pure air to pure fuel. It was found that H/O and C2/CN intensity ratios depend monotonically on the mole fraction of methane in the ranges of 0.0–0.8 and 0.3–1.0 respectively. The presence of a flame within the laser beam led to significant measurement deterioration relative to the corresponding non-reacting flows. This could lead to increased measurement uncertainty, and was therefore corrected by increasing the laser pulse energy and applying a proposed data processing method; the proposed correction method was able to reduce the equivalence ratio measurement uncertainty to be within 10% for mixtures with methane mole fractions lower than 50% and within 15% for mixtures with higher methane mole fraction. The established correlations between the intensity ratios of H/O and C2/CN with local air-fuel ratio allowed measurement of the spatial gradient of air-fuel ratio across opposed jet diffusion flames. The LIBS measurements of air-fuel ratio in non-premixed flames were finally compared successfully with CHEMKIN simulations, which demonstrates the ability of LIBS to accurately measure the spatial gradient of the air-fuel ratio.

Measurement uncertainty quantification for myocardial perfusion using cardiac positron emission tomography imaging

Perfusion, the flow of blood, and hence oxygen, is essential to the functioning of the heart. Reduced perfusion (or ischemia), is a reliable indicator of the presence of significant obstructive coronary artery disease (CAD), which is one of the biggest causes of death in Europe. Myocardial perfusion imaging is a non-invasive technique used in the diagnosis, management and prognosis of CAD and is a key component in the triage of patients into treatment and non-treatment groups. Cardiac positron emission tomography (PET) is an imaging technique with high sensitivity and specificity to CAD, however perfusion measurements are difficult to calibrate against a common reference standard, and confidence in them is generally not quantified in terms of measurement uncertainty. There are a number of steps involved in measuring perfusion using cardiac PET—from patient preparation to data analysis—each associated with potential sources of uncertainty. The absence of measurement uncertainty quantification can lead to inaccuracies in measurement results, a lack of comparability between devices or scanning facilities, and is likely to be detrimental to a decision-making process. In this paper, we identify some of the sources of measurement uncertainty in the cardiac PET perfusion measurement pipeline. We assess their relative contribution by performing a sensitivity analysis using experimental data of a flow phantom acquired on a PET scanner. The results of this analysis will inform users of how parameter choices in their imaging pipeline affect the output of their measurements, and serves as a starting point to develop an uncertainty quantification method.

Flow measurement uncertainty quantification for building central cooling systems with multiple water-cooled chillers using a Bayesian approach

Measurement uncertainty has significant negative impacts on the operation and control of heating, ventilation and air conditioning systems. It is a big challenge and should be solved urgently. Existing studies focus on reducing the impacts of measurement uncertainty by developing uncertainty tolerant methods without quantifying the measurement uncertainties themselves. They therefore fail to fundamentally solve them. This study aims to directly quantify the measurement uncertainties of water flow meters in multiple water-cooled chiller systems using a Bayesian approach. A measurement uncertainty quantification strategy is proposed based on Bayesian inference and energy balance models, and the Markov chain Monto Carlo method is used to achieve the strategy. The site data collected from a chiller system are used to test the strategy. Four simulation tests with different levels of measurement uncertainty are conducted to further test and systematically validate the strategy. Test results show that the measurement uncertainties (both systematic and random uncertainties) of the water flow meters in the chiller systems can be quantified effectively and with acceptable accuracy. The strategy performs very well in quantifying random uncertainties of flow meters, and the relative errors range from 0% to 12.8%. The performance of the strategy in quantifying systematic uncertainties is also satisfactory, and the relative errors range from 0.1% to 36.57%. The proposed strategy is able to quantify measurement uncertainties and can be used to optimize the control of chiller systems and improve the reliability of chiller systems.

Measurement of the 239Pu(n,f)/235U(n,f) Cross-Section Ratio with the NIFFTE fission Time Projection Chamber

The 239Pu(n,f)/235U(n,f) cross-section ratio has been measured with the fission Time Projection Chamber (fissionTPC) from 100 keV to 100 MeV. The fissionTPC provides three-dimensional reconstruction of fission-fragment ionization profiles, allowing for a precise quantification of measurement uncertainties. The measurement was performed at the Los Alamos Neutron Science Center which provides a pulsed white source of neutrons. The data are recommended to be used as a cross-section ratio shape. A discussion of the status of the absolute normalization and comparisons to ENDF evaluations and previous measurements is included.

Experimental quantification of measurement uncertainty and other verification criteria for analytical test methods

If the measurement produces a numerical result (or the stated result is based on a numerical result), then it is necessary to evaluate the uncertainty of these numerical results. If the test methodology does not provide for a rigorous, metrologically and statistically reliable estimate of measurement uncertainty, the test laboratory should attempt to reasonably assess the uncertainty of the measurement results. This is applicable in the case of test methods both rational and empirical. As well as in the laboratory activities, verification of test methods plays an important role in order to guarantee the competence of the laboratory by checking for the criteria of Kohren, Grabbs and others. In cases where test results are not numerically expressed or based on numerical data (e.g., fit/unsuitable or positive/negative, or based on visual or sensory perceptions or other forms of quality analysis), estimates of uncertainty or other variability of results are not required. Nevertheless, laboratories are advised to have an idea of the variability of the results, if possible. The importance of the uncertainty of qualitative test results is undeniable, as is the fact that the necessary statistical methodology (procedure) exists for the calculations. However, due to the complexity of the issue and the inconsistency in the approaches, it is not currently necessary for laboratories to assess the uncertainty of qualitative test results. However, this issue needs to be considered. The article discusses estimates of uncertainty of analytical measurements taking into account the requirements of regulatory documents. The criteria for verification of measurement methodology based on experimental works are also considered and analyzed.

Uncertainties in measurements of leaf optical properties are small compared to the biological variation within and between individuals of European beech

The measurement of leaf optical properties (LOP) using reflectance and scattering properties of light allows a continuous, time-resolved, and rapid characterization of many species traits including water status, chemical composition, and leaf structure. Variation in trait values expressed by individuals result from a combination of biological and environmental variations. Such species trait variations are increasingly recognized as drivers and responses of biodiversity and ecosystem properties. However, little has been done to comprehensively characterize or monitor such variation using leaf reflectance, where emphasis is more often on species average values. Furthermore, although a variety of platforms and protocols exist for the estimation of leaf reflectance, there is neither a standard method, nor a best practise of treating measurement uncertainty which has yet been collectively adopted. In this study, we investigate what level of uncertainty can be accepted when measuring leaf reflectance while ensuring the detection of species trait variation at several levels: within individuals, over time, between individuals, and between populations. As a study species, we use an economically and ecologically important dominant European tree species, namely Fagus sylvatica. We first use fabrics as standard material to quantify measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation. We then quantify spectrally resolved variation in reflectance from F. sylvatica leaves. We show that the measurement uncertainty associated with leaf reflectance, estimated using a field spectroradiometer with attached leaf clip, represents on average a small portion of the spectral variation within a single individual sampled over one growing season (2.7 ± 1.7%), or between individuals sampled over one week (1.5 ± 1.3% or 3.4 ± 1.7%, respectively) in a set of monitored F. sylvatica trees located in Swiss and French forests. In all forests, the spectral variation between individuals exceeded the spectral variation of a single individual at the time of the measurement. However, measurements of variation within individuals at different canopy positions over time indicate that sampling design (e.g., standardized sampling, and sample size) strongly impacts our ability to measure between-individual variation. We suggest best practice approaches toward a standardized protocol to allow for rigorous quantification of species trait variation using leaf reflectance.