Uncertainty Assessment Research Articles

Dynamic effects of internal and external policy uncertainty on consumption: exploring with the perspective of trade shares

PurposeMacroeconomic policy shocks have consistently provoked debate across global economies, given their significant effect on economic growth, particularly by affecting markets and employment through changes in consumer behavior. Policymakers need proper abatement measures for both internal and external uncertainties.Design/methodology/approachThe present study aims to analyze the impacts of internal and external policy uncertainty on consumption in 22 countries with their trading shares with each other from 2010q1 to 2021q4. In this regard, for external policy shocks the study constructed an index for each country based on their bilateral trade shares a weighted average of internal policy uncertainty.FindingsBy applying the panel ARDL, a U-shaped relationship between consumption and policy shocks is observed. Further, for the county wise assessment of internal and external policy uncertainty on domestic consumption is assessed and it has shown to be negative in most of countries. Gross effects are also revealed where internal policy shocks have more influence than external policy shocks as countries can diversify external risks from their trade portfolio. Other determinates of consumption like income and exchange rate have positive effects, while interest rate and inflation have negative influences.Originality/valueThe outcomes provide internal and external policy insights for consumption stabilization.

Influence of statistical methods on lower limits of dose estimation in biological dosimetry

Purpose In cases of radiological or nuclear events, biological dosimetry enables decisions whether an individual was exposed to ionizing radiation and the estimation of the dose. Several statistical methods are used to assess uncertainties. The stringency of the applied method has an impact on the lowest dose that can be detected. To obtain reliable and comparable results, it is crucial to harmonize the applied statistical methods. Materials and methods The decision threshold and detection limit of the statistical methods were derived for variable cell numbers. The coverage of the 95% confidence intervals as well as the false-positive and false-negative rates of the methods were compared based on simulations. The evaluated methods included a graphical method, the propagation of errors and a Bayesian method. Results The minimum resolvable doses, the doses at the detection limit and the coverage were relatively variable between the compared methods. The Bayesian method showed the best coverage, lowest resolvable doses and had false-positive rates close to 5%. The graphical method with the combination of two 83% confidence intervals also showed promising results. The other methods were either too conservative or underestimated the uncertainties for some doses or cell numbers. Conclusions The assessment of the lower dose limits is a central part of biological dosimetry and the applied statistical methods have a strong influence on the interpretation of the results. Simulations enable comparisons between methods and provide important information for the harmonization and standardization of the uncertainty assessment.

Uncertainty in determining carbon dioxide removal potential of biochar

Abstract A quantitative and systematic assessment of uncertainty in life-cycle assessment is critical to informing sustainable development of carbon dioxide removal (CDR) technologies. Biochar is the most commonly sold form of CDR to date, and it can be used in applications ranging from concrete to agricultural soil amendments. Previous analyses of biochar rely on modeled or estimated life-cycle data and suggest a cradle-to-gate range of 0.20–1.3 kg CO2 net removal per kg of biomass feedstock, driven by differences in energy consumption, pyrolysis temperature, and feedstock sourcing. Herein, we quantify the distribution of CDR possible for biochar production with a compositional life-cycle inventory model paired with scenario-aware Monte Carlo simulation in a “best practice” (incorporating lower transportation distances, high pyrolysis temperatures, high energy efficiency, recapture of energy for drying and pyrolysis energy requirements, and co-generation of heat and electricity) and “poor practice” (higher transportation distances, lower pyrolysis temperatures, low energy efficiency, natural gas for energy requirements, and no energy recovery) scenarios. In the best-practice scenario, cradle-to-gate CDR (which is representative of the upper limit of removal across the entire life cycle) is highly certain, with a median removal of 1.4 kg of CO2e / kg biomass and results in net removal across the entire distribution. In contrast, the poor-practice scenario results in median net emissions of 0.090 kg CO2e / kg biomass. Whether this scenario emits (66% likelihood) or removes (34% likelihood) carbon dioxide is highly uncertain. The emission intensity of energy inputs to the pyrolysis process and whether the bio-oil co-product is used as a chemical feedstock or combusted are critical factors impacting the net carbon dioxide emissions of biochar production, together responsible for 98% of the difference between the best- and poor-practice scenarios.

Exploring transformer reliability in clinically significant prostate cancer segmentation: A comprehensive in-depth investigation.

Despite the growing prominence of transformers in medical image segmentation, their application to clinically significant prostate cancer (csPCa) has been overlooked. Minimal attention has been paid to domain shift analysis and uncertainty assessment, critical for safely implementing computer-aided diagnosis (CAD) systems. Domain shift in medical imagery refers to differences between the data used to train a model and the data evaluated later, arising from variations in imaging equipment, protocols, patient populations, and acquisition noise. While recent models enhance in-domain performance, areas such as robustness and uncertainty estimation in out-of-domain distributions have received limited investigation, creating indecisiveness about model reliability. In contrast, our study addresses csPCa at voxel, lesion, and image levels, investigating models from traditional U-Net to cutting-edge transformers. We focus on four key points: robustness, calibration, out-of-distribution (OOD), and misclassification detection (MD). Findings show that transformer-based models exhibit enhanced robustness at image and lesion levels, both in and out of domain. However, this improvement is not fully translated to the voxel level, where Convolutional Neural Networks (CNNs) outperform in most robustness metrics. Regarding uncertainty, hybrid transformers and transformer encoders performed better, but this trend depends on misclassification or out-of-distribution tasks.

Psychometric properties of the American version of the Chronic Uncertainty scale: long and short version (CU-40; CU-20)

BackgroundUncertainty is a widespread phenomenon experienced worldwide. The bulk of existing research to date has focused on transitory or acute experiences of uncertainty, often in the particular context of illness. The current study evaluated the psychometric properties of the cross-contextual 40-item (long) and 20-item (short) Chronic Uncertainty scale (CU-40; CU-20).MethodsA U.S. sample of 1083 participants (55% female) was recruited via Mechanical Turk (MTurk). Exploratory factor analysis revealed a six-factor model.ResultsResults of the Confirmatory factor analysis showed good model fit for the original and a slightly better model fit for the modified version of the CU-40 and CU-20. Slightly different item-to-factor attributions were suggested for the questionnaires. Internal consistencies were good for both models.ConclusionThe evidence suggests that the CU scale offers psychometrically sound assessments of chronic uncertainty across a range of dimensions. Further assessments and implementations of the CU in different contexts using diverse samples are encouraged to test the efficacy of the CU measures as screening tools of general aspects of chronic uncertainty.

On the importance of discharge observation uncertainty when interpreting hydrological model performance

Abstract. For users of hydrological models, the suitability of models can depend on how well their simulated outputs align with observed discharge. This study emphasizes the crucial role of factoring in discharge observation uncertainty when assessing the performance of hydrological models. We introduce an ad hoc approach, implemented through the eWaterCycle platform, to evaluate the significance of differences in model performance while considering the uncertainty associated with discharge observations. The analysis of the results encompasses 299 catchments from the Catchment Attributes and MEteorology for Large-sample Studies Great Britain (CAMELS-GB) large-sample catchment dataset, addressing three practical use cases for model users. These use cases involve assessing the impact of additional calibration on model performance using discharge observations, conducting conventional model comparisons, and examining how the variations in discharge simulations resulting from model structural differences compare with the uncertainties inherent in discharge observations. Based on the 5th to 95th percentile range of observed flow, our results highlight the substantial influence of discharge observation uncertainty on interpreting model performance differences. Specifically, when comparing model performance before and after additional calibration, we find that, in 98 out of 299 instances, the simulation differences fall within the bounds of discharge observation uncertainty. This underscores the inadequacy of neglecting discharge observation uncertainty during calibration and subsequent evaluation processes. Furthermore, in the model comparison use case, we identify numerous instances where observation uncertainty masks discernible differences in model performance, underscoring the necessity of accounting for this uncertainty in model selection procedures. While our assessment of model structural uncertainty generally indicates that structural differences often exceed observation uncertainty estimates, a few exceptions exist. The comparison of individual conceptual hydrological models suggests no clear trends between model complexity and subsequent model simulations falling within the uncertainty bounds of discharge observations. Based on these findings, we advocate integrating discharge observation uncertainty into the calibration process and the reporting of hydrological model performance, as has been done in this study. This integration ensures more accurate, robust, and insightful assessments of model performance, thereby improving the reliability and applicability of hydrological modelling outcomes for model users.

Assessment of measurement uncertainty associated with fracture toughness calculation based on energy release rate in spherical indentation tests

This work aims to evaluate the uncertainty associated with the fracture toughness of 4130 M steel obtained via instrumented indentation tests and the methodology proposed by Zhang, Wang and Wang (2019). These authors take the energy release rate as a basis to determine fracture toughness. In this study, the uncertainty was assessed using the Guide to the Expression of Uncertainty of Measurement (GUM) and Monte Carlo methods. For uncertainty assessment, all measurands and input variables were identified based on the equations proposed by these authors. Then, the standard uncertainty associated with each input variable was calculated by applying the GUM method. To determine the uncertainty associated with the energy release rate JSIT the Monte Carlo method was applied. The results obtained showed that the expanded uncertainty associated with JSIT considering three tests was 1.486 N.mm−1. This uncertainty represents 7.25 % of the average JSIT value (20.490 N.mm−1). The expanded uncertainty associated with KIC was 7.520 MPa.m0.5. These represent 3.82 % of the average KIC (196.562 MPa.m0.5) value obtained. This paper demonstrates that despite the complexity of the mathematical equation proposed by Zhang, Wang and Wang (2019), to obtain fracture toughness via instrumented indentation tests, the uncertainty associated can be successfully accessed by combining the GUM and Monte Carlo methods. This work showed that the application of the instrumented indentation technique with the methodology proposed by Zhang, Wang and Wang (2019) provided fracture toughness values with excellent quality under the experimental conditions used in this study.

Model uncertainty assessment for symmetric and right-skewed distributions

In actuarial modeling, right-skewed distributions are of paramount importance. Typically, they have the property of becoming unimodal and symmetric after applying some increasing concave transformation T (e.g. log-transformation). In what follows, we refer to them as T -unimodal T -symmetric distributions. In this paper, we derive attainable bounds for the Value-at-Risk for such distributions when some partial information is available, such as information on the support, median, or certain moments. We also derive explicit upper and lower bounds for the Range Value-at-Risk under knowledge of unimodality, symmetry, mean, variance, and possibly bounded support. In passing, we provide a generalization of the Gauss inequality for symmetric distributions with known support. We show how these bounds improve on bounds available in the literature using a real-world automobile insurance claims dataset.

Multicity accountability and uncertainty assessment of the impacts of regulations on air quality in Atlanta, New York City, and Southern California

Multiple regulations have been promulgated to improve air quality, and previous studies have used an accountability chain to evaluate the effects of these regulations on emission levels, air quality, and human health. However, quantifying these impacts through the accountability chain is complex due to interactions between multiple factors that can influence the efficacy of control policies and introduce uncertainties at each step. We evaluated and quantified the impact of emission controls on electricity generating units (EGU) and motor vehicle sources on emissions and air quality via Generalized Additive Models. These GAMs have minimal bias (around 10−5 to 10−2 μg/m3 or ppbV) and r2 values for daily concentrations ranging from 0.4 to 0.7 Counterfactual air pollutant concentrations, in the absence of EGU and mobile source regulations, were calculated using estimated counterfactual emissions for the period 2005 to 2019 in Atlanta, New York City, and California's South Coast Air Basin. Counterfactual air pollutant concentrations indicated that the effects of regulations on air pollutants varied depending on the season and location. Predicted counterfactual air pollutant concentrations were generally 2–12 times higher than the measured concentrations at these sites, except for ozone. The impact of regulations on ozone concentrations typically resulted in reduced peak ozone values in the summer, but increased concentrations in the winter. Monte Carlo modeling found small to modest uncertainties, depending on the pollutant, location and regulations assessed. Counterfactual concentrations predicted in this project will be used in the assessment of the trends of toxicity in PM2.5.

Critical appraisal of the Expert Knowledge Elicitation (EKE) methodology to identify uncertainties in building cumulative assessment groups for craniofacial alterations

The European Food Safety Authority (EFSA) conducted a retrospective cumulative dietary risk assessment (CRA) on active substances (AS) and metabolites included in the plant protection products registered in Europe which could provoke craniofacial alterations. Two Cumulative Assessment groups (CAGs) were established: one for alterations due to abnormal skeletal development (CAG-DAC) and one for head soft tissue alterations and brain neural tube defects (CAG-DAH).The probability that each substance is correctly assigned to the specific CAGs (CAG-membership probability) was assessed using weight of evidence and expert knowledge elicitation (EKE) techniques conducted for the six substances identified as risk drivers in each CAG. Four out of the six substances allocated to the CAG-DAC or to the CAG-DAH presented a large interval of uncertainty, with probability of belonging to the attributed CAG between 10 to 70% or 33 to 90%, which makes it difficult to determine if the substances truly belong to the CAG.In the present work the probability ranges of each risk driver were reassigned according to the approximate probability scale recommended in the EFSA guidance on uncertainty analysis. It is proposed that AS with a high probability are to be included in the CAG and those with a low probability removed from the CAG. For compounds with very large probability ranges, uncertainty assessments would have to be redone to reach narrower probability ranges. Finally, whenever recent decisions on reproduction toxicity classifications made by the European Chemical Agency (ECHA) are available, these should be used to conclude on CAG memberships.

Multivariate Moment Least-Squares Variance Estimators for Reversible Markov Chains

Markov chain Monte Carlo (MCMC) is a commonly used method for approximating expectations with respect to probability distributions. Uncertainty assessment for MCMC estimators is essential in practical applications. Moreover, for multivariate functions of a Markov chain, it is important to estimate not only the auto-correlation for each component but also to estimate cross-correlations, in order to better assess sample quality, improve estimates of effective sample size, and use more effective stopping rules. Berg and Song introduced the moment least squares (momentLS) estimator, a shape-constrained estimator for the autocovariance sequence from a reversible Markov chain, for univariate functions of the Markov chain. Based on this sequence estimator, they proposed an estimator of the asymptotic variance of the sample mean from MCMC samples. In this study, we propose novel autocovariance sequence and asymptotic variance estimators for Markov chain functions with multiple components, based on the univariate momentLS estimators from Berg and Song. We demonstrate strong consistency of the proposed auto(cross)-covariance sequence and asymptotic variance matrix estimators. We conduct empirical comparisons of our method with other state-of-the-art approaches on simulated and real-data examples, using popular samplers including the random-walk Metropolis sampler and the No-U-Turn sampler from STAN. Supplemental materials for this article are available online.

Electrical resistivity tomography inversion combining deep variational autoencoder and stochastic adaptive sampling

Geophysical methods, such as electrical resistivity tomography (ERT), can be used to imaging the near-surface electrical resistivity as field measurements depend on the subsurface porosity, water saturation and fluid salinity. ERT has been widely applied to investigate mineral and groundwater resources, and in archaeological, environmental, and engineering studies. The prediction of subsurface electrical conductivity from ERT data requires solving a geophysical inverse problem. For near-surface characterization studies, this is often accomplished with deterministic inverse methods. These methods linearize the problem around an initial solution, and their smoothness depends on an imposed a priori spatial regularization term. Depending on this parameterization, these methodologies might struggle to capture the natural variability of the subsurface. Moreover, deterministic solutions have limited capabilities for uncertainty assessment. On the contrary, stochastic inverse methods can assess uncertainties by predicting multiple model realizations that fit similarly the recorded ERT data. However, they are often more computationally expensive than deterministic solutions. Deep learning algorithms based on deep generative models have been used to re-parametrize model and data spaces into low-dimensional domains and efficiently solve geophysical inverse problems. However, within this context, uncertainty assessment is challenging. We propose a deep convolutional variational autoencoder (VAE) coupled with stochastic adaptive optimization to perform stochastic ERT inversion. Geostatistical simulations of electrical resistivity are used as training data set of the VAE. After training, the VAE generates electrical resistivity models that reproduce the statistics and spatial continuity patterns of the training data set. Then, the VAE latent space is iteratively perturbed and updated with adaptive stochastic sampling based on the misfit between observed and predicted ERT data. The proposed methodology is illustrated in two-dimensional synthetic and real data sets to illustrate the ability of the proposed method to predict reliable electrical resistivity models while generating multiple possible scenarios for uncertainty assessment.

An Assessment of the Seasonal Uncertainty of Microwave L-Band Satellite Soil Moisture Products in Jiangsu Province, China

An Assessment of the Seasonal Uncertainty of Microwave L-Band Satellite Soil Moisture Products in Jiangsu Province, China

Analysis of influencing factors and uncertainty assessment of high-pressure natural gas primary standard facility

The mass-time high-pressure natural gas flow standard device is the highest level natural gas flow measurement standard in China. It is mainly used to reproduce the mass flow of high-pressure natural gas and transfer the value. Due to its important position in the value traceability transmission systems, it is of great significance to improve its uncertainty. This article first introduces the working principle and component equipment of mass-time high-pressure natural gas flow standard device, and the influencing factors and control measures of the recurring mass flow uncertainty are given. Then the calculation model and uncertainty evaluation model of the mass flow rate of the standard device and the discharge coefficient of the critical flow nozzle are given. Finally, based on the calculation model, two critical flow nozzles are calibrated and tested, and their repeatability and stability indicators are discussed and analyzed. After analysis, it can be concluded that the measurement performance of the mass-time high-pressure natural gas flow standard device is stable, which can make the uncertainty of the reproduced mass flow rate less than 0.05 % (k = 2), and the uncertainty of the calibrated discharge coefficient of critical flow nozzle is less than 0.1 %(k = 2).

INCREASE OF TECHNOLOGICAL INDICATORS USED IN THE ENERGY INDUSTRY

When evaluating the suitability of using coal in various industries, it is subjected to technical analysis and a number of special technological studies. The obtained results in combination with elemental and group analysis allow choosing the most rational and economically efficient directions of their use, which plays not the least role in determining the economic significance of coal mining deposits. The work is based on theoretical research methods based on the basics of probability theory and mathematical statistics - to improve the existing methods of processing results and assessing the uncertainty of measurements; theories of accuracy - for the development of procedures for assessing the uncertainty of measurements of the quality parameters of solid mineral fuel during its tests; statistical modeling methods - to check the reliability of the developed provisions. A generalized parameter of the quality of solid mineral fuel has been synthesized, which makes it possible to evaluate its quality based on the results of tests conducted in coal chemical laboratories. improvement of methods of testing solid mineral fuel according to quality parameters by developing uncertainty assessment procedures.

A Measurement Device for the Normal Spectral Emissivity of Materials Under Low-Temperature and Vacuum Conditions

Based on parallel light, a device for measuring sample emissivity under vacuum and low temperature was established. In this paper, a new emissivity measurement formula was designed, which replaces the derivation of Kirchhoff’s law of thermal radiation. The device is designed with a light-weight blackbody, an improved cooling speed, and an improved PID temperature control system to achieve a good temperature stability, close to 1 mK. The device is capable of measuring within a wavelength range of 4 μm to 16 μm. The results of the uncertainty assessment show that the uncertainty of the normal emissivity is better than 1.6% in the range of 4 μm to 7 μm, and better than 0.6% in the range of 7 μm to 14 μm (k = 1) at 0 °C.

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.

Comparing Two Geostatistical Simulation Algorithms for Modelling the Spatial Uncertainty of Texture in Forest Soils

Uncertainty assessment is an essential part of modeling and mapping the spatial variability of key soil properties, such as texture. The study aimed to compare sequential Gaussian simulation (SGS) and turning bands simulation (TBS) for assessing the uncertainty in unknown values of the textural fractions accounting for their compositional nature. The study area was a forest catchment (1.39 km2) with soils classified as Typic Xerumbrepts and Ultic Haploxeralf. Samples were collected at 135 locations (0.20 m depth) according to a design developed using a spatial simulated annealing algorithm. Isometric log-ratio (ilr) was used to transform the three textural fractions into a two-dimensional real vector of coordinates ilr.1 and ilr.2, then 100 realizations were simulated using SGS and TBS. The realizations obtained by SGS and TBS showed a strong similarity in reproducing the distribution of ilr.1 and ilr.2 with minimal differences in average conditional variances of all grid nodes. The variograms of ilr.1 and ilr.2 coordinates were better reproduced by the realizations obtained by TBS. Similar results in reproducing the texture data statistics by both algorithms of simulation were obtained. The maps of expected values and standard deviations of the three soil textural fractions obtained by SGS and TBS showed no notable visual differences or visual artifacts. The realizations obtained by SGS and TBS showed a strong similarity in reproducing the distribution of isometric log-ratio coordinates (ilr.1 and ilr.2). Overall, their variograms and data were better reproduced by the realizations obtained by TBS.

Evaluating the chain of uncertainties in the 3D geological modelling workflow

Geological models are used for a range of applications relevant for engineering geology and the demands for reliable geological models with realistic uncertainty assessments are therefore increasing. The geological modelling workflow is divided into multiple steps, each associated with uncertainties. Often however, many of these sources of uncertainty are overlooked, which risks underestimating the uncertainties of the final model. The main reason is that most of the steps in the geological modelling workflow are subjective to some degree. Thus, the possible sources of uncertainty in geological modelling are theoretically infinite, and without a trustworthy conceptual model to outline the expected geological structures and lithologies, the uncertainty assessment of the resulting model will likewise be unreliable. In this paper, we describe the chain of uncertainties in the geological modelling workflow and showcase some of the most important sources of uncertainties through practical modelling examples from two different model areas. The paper also presents and discuss a method to conduct qualitative uncertainty assessment, which is conducted by the modeler and based on expert evaluation and prioritization of the different sources of uncertainty. The practical use of the uncertainty assessment method is exemplified in the last two examples, representing a local-scale and a large-scale model, respectively. All four examples are from Danish geological models that have been constructed as interpretation-based layer-models. However, the considerations regarding the uncertainties in the chain of the geological modelling workflow are useful regardless of the modelling method.

Panel-based assessment of ecosystem condition as a platform for adaptive and knowledge driven management.

Ecosystems are subjected to increasing exposure to multiple anthropogenic drivers. This has led to the development of national and international accounting systems describing the condition of ecosystems, often based on few, highly aggregated indicators. Such accounting systems would benefit from a stronger theoretical and empirical underpinning of ecosystem dynamics. Operational tools for ecosystem management require understanding of natural ecosystem dynamics, consideration of uncertainty at all levels, means for quantifying driver-response relationships behind observed and anticipated future trajectories of change, and an efficient and transparent synthesis to inform knowledge-driven decision processes. There is hence a gap between highly aggregated indicator-based accounting tools and the need for explicit understanding and assessment of the links between multiple drivers and ecosystem condition as a foundation for informed and adaptive ecosystem management. We describe here an approach termed PAEC (Panel-based Assessment of Ecosystem Condition) for combining quantitative and qualitative elements of evidence and uncertainties into an integrated assessment of ecosystem condition at spatial scales relevant to management and monitoring. The PAEC protocol is founded on explicit predictions, termed phenomena, of how components of ecosystem structure and functions are changing as a result of acting drivers. The protocol tests these predictions with observations and combines these tests to assess the change in the condition of the ecosystem as a whole. PAEC includes explicit, quantitative or qualitative, assessments of uncertainty at different levels and integrates these in the final assessment. As proofs-of-concept we summarize the application of the PAEC protocol to a marine and a terrestrial ecosystem in Norway.