Quantitative Uncertainty Research Articles

Quantitative SPECT/CT imaging of lead-212: a phantom study

BackgroundLead-212 (212Pb) is a promising radionuclide for targeted therapy, as it decays to α-particle emitter bismuth-212 (212Bi) via β-particle emission. This extends the problematic short half-life of 212Bi. In preparation for upcoming clinical trials with 212Pb, the feasibility of quantitative single photon-emission computed tomography/computed tomography (SPECT/CT) imaging of 212Pb was studied, with the purpose to explore the possibility of individualised patient dosimetric estimation.ResultsBoth acquisition parameters (combining two different energy windows and two different collimators) and iterative reconstruction parameters (varying the iterations x subsets between 10 × 1, 15 × 1, 30 × 1, 30 × 2, 30 × 3, 30 × 4, and 30 × 30) were investigated to evaluate visual quality and quantitative uncertainties based on phantom images. Calibration factors were determined using a homogeneous phantom and were stable when the total activity imaged exceeded 1 MBq for all the imaging protocols studied, but they increased sharply as the activity decayed below 1 MBq. Both a 20% window centred on 239 keV and a 40% window on 79 keV, with dual scatter windows of 5% and 20%, respectively, could be used. Visual quality at the lowest activity concentrations was improved with the High Energy collimator and the 79 keV energy window. Fractional uncertainty in the activity quantitation, including uncertainties from calibration factors and small volume effects, in spheres of 2.6 ml in the NEMA phantom was 16–21% for all protocols with the 30 × 4 filtered reconstruction except the High Energy collimator with the 239 keV energy window. Quantitative analysis was possible both with and without filters, but the visual quality of the images improved with a filter.ConclusionsOnly minor differences were observed between the imaging protocols which were all determined suitable for quantitative imaging of 212Pb. As uncertainties generally decreased with increasing iterative updates in the reconstruction and recovery curves did not converge with few iterations, a high number of reconstruction updates are recommended for quantitative imaging.

Idealized 2D Cloud-resolving Simulations for Tidally Locked Habitable Planets

Cloud is critical for planetary climate and habitability, but it is also one of the most challenging aspects of studying planets in and beyond the solar system. Here we use a cloud-resolving model (CRM) with high resolution (2 km) in a 2D configuration to simulate the clouds and circulation on tidally locked aquaplanets. We find that the substellar area is covered by deep convective clouds, the nightside is dominated by low-level clouds, and the two are linked by a global-scale Walker circulation. We further find that uniform surface warming causes the substellar cloud width to decrease, but a reduction in the day–night surface temperature contrast or an increase in the longwave radiative cooling rate causes the substellar cloud width to increase. These relationships can be roughly interpreted in accordance with simple thermodynamic theories. Comparing the results between the CRM and the global 3D general circulation model (GCM), we find that they are qualitatively consistent, including the Walker circulation, the substellar clouds, and the responses of the substellar ascending area and strength to changes in the surface temperature or in its zonal contrast. But large quantitative differences exist, such as the magnitude of the cloud water path, the cloud width, and their responses to external forcings. These results increase our confidence in using GCMs to model exoplanetary climates, although large quantitative uncertainties are always likely to exist. Future work is required to use 3D CRMs with realistic radiative transfer and the Coriolis force to examine the clouds and climates of tidally locked planets.

Validation of a method for the quantitative determination of narcotic and psychotropic substances in urine by UHPLC–MS/MS

Objectives. To validate a new method for the quantitative determination of 31 potent and narcotic substances and their metabolites in urine that meets the requirements of ISO/IEC 17025 using a fast and highly sensitive method of chromato-mass spectrometry with a view to introducing such a method into the routine practice of the National Anti-Doping Laboratory of the Lomonosov Moscow State University (NADL MSU).Methods. Urine samples soldered with standard solutions were analyzed using ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC–MS/MS).Results. Diagnostic precursor/ion-product pairs and collision energies were established to allow unambiguous identification of the analyzed substances. During sample preparation, hydrolysis conditions were optimized. Selectivity, linearity, limits of qualitative determination, limit of quantitative determination (established under the contract with the customer firm), matrix effect, and measurement uncertainty were defined. Systematized data grouped by classes of analytes are given in the final table.Conclusions. The important advantages of the presented technique are the absence of complex and lengthy sample preparation, as well as the short time of the analysis method (about 10 min), which can significantly reduce duration along with labor and analysis costs. The addition of new analytes will ensure the versatility of the technique, as well as expanding its scope.

Horizon auto-picking with quantitative uncertainty evaluation by using a modified VQ-VAE framework

Abstract In this paper, we propose a novel approach of deep-learning-based seismic horizon auto-picking that introduces a modified vector quantized variational autoencoder (VQ-VAE) framework to improve the accuracy of seismic horizon interpretation and, for the first time, quantitatively evaluate the uncertainty of the auto-picked horizon by exploiting the concept of entropy. Compared with the conventional VQ-VAE approach, the proposed method not only modifies the VQ-VAE model with more deep-learning channels at each layer of the network to enhance the performance of horizon auto-picking within the VQ-VAE framework, but also extends the 1D seismic labels with more continuous samplings within a single trace to boost the stability of auto-picked horizon in geologically complex settings and also significantly suppress the resulting uncertainty. To further improve the resulting accuracy in geologically complex settings, we introduce the directional structure tensor to extract a more reliable initial horizon and, moreover, a dilated horizon searching strategy to extend the capacity of the proposed method in dealing with the large fault displacement and reducing the computational cost simultaneously. Additionally, the resulting uncertainty quantitatively measured by entropy can also serve as an effective indicator to enable a further refinement of the auto-picked result accordingly. Both 2D example and 3D field applications are carried out to validate the effectiveness of the proposed method.

Opinion on the Safety of BioProtein® by the Scientific Panel on Animal Feed of the Norwegian Scientific Committee for Food Safety

Opinion on the Safety of BioProtein® by the Scientific Panel on Animal Feed of the Norwegian Scientific Committee for Food Safety

Quantitative Source Apportionment and Uncertainty Analysis of Heavy Metal(loid)s in the Topsoil of the Nansi Lake Nature Reserve

There are numerous coal mines around the Nansi Lake Provincial Nature Reserve, and the mineral resources are extremely rich. Therefore, it is necessary to effectively assess the impact of mining activities on the topsoil. Based on a focused investigation of the contamination status and ecological risks of the Nansi Lake Nature Reserve assisted by GIS, principal component analysis was combined with positive matrix factorization to quantitatively identify the sources and contributions of heavy metal(loid)s in the topsoil before conducting uncertainty analysis. The results showed that coal mining caused higher Cu, Zn, and As contamination levels, while Hg and Cd had higher eco-toxicity and biological sensitivity. Meanwhile, principal component analysis (PCA) and positive matrix factorization (PMF) modeling displayed that Hg (59.2%) was primarily generated by industrial sources (fossil fuel combustion and mercury-containing wastewater); As (70.2%), Ni (65.6%), Cr (63.5%), Pb (61.3%), Cu (60.3%), and Zn (55.8%) were generated mainly from coal mining and processing, coal fossil fuel combustion, and coal gangue dumps; Cd (79.8%) came mainly from agricultural sources. Through uncertainty analysis, the contribution of contamination sources to the heavy metal(loid)s in the topsoil, as estimated by the PMF model, was shown to be quite different. Moreover, heavy metal(loid)s with lower contributions had errors in source identification and factor quantification. This study innovatively warned management to control the hazards of heavy metal(loid)s caused by mining engineering to protect the environment of the Nansi Lake Nature Reserve and revealed the potential harmful pathways of heavy metal(loid)s.

Dynamic Uncertainty Quantification and Risk Prediction Based on the Grey Mathematics and Outcrossing Theory

Embarked from the practical conditions of small samples in time-invariant and time-variant uncertainties, a complete non-probabilistic analysis procedure containing uncertainty quantification, uncertainty propagation, and reliability evaluation is presented in this paper. Firstly, the Grey systematic approach is proposed to determine the boundary laws of static intervals and dynamic interval processes. Through a combination of the policies of the second-order Taylor expansion and the smallest parametric interval set, the structural response histories via quantitative uncertainty results are further confirmed. Additionally, according to the first-passage idea from classical random process theory, the study on the time-dependent reliability measurement on the basis of the interval process model is carried out to achieve a more elaborate estimation for structural safety during its whole life cycle. A numerical example and one experimental application are eventually discussed for demonstration of the usage and reasonability of the methodology developed.

Quantitative Uncertainty Principles Associated with the k-Generalized Stockwell Transform

Quantitative Uncertainty Principles Associated with the k-Generalized Stockwell Transform

Normal cloud model theory-based comprehensive fuzzy assessment of wooden pagoda safety

• It's the first time to apply normal cloud model into wooden pagoda safety assessment. • Two-way mapping of the "quantitative description-qualitative concept". • A four-level safety assessment index system. Currently the safety assessment of wood structure ancient buildings is mainly qualitative description, ignoring accurate quantitative calculation, and there are uncertain factors such as fuzziness and randomness that affect the results in the assessment process. Therefore, based on the cloud model theory of transformation between qualitative and quantitative uncertainties, this paper proposes a normal cloud model-based safety assessment approach for the protection research of wooden pagoda. A multi-level assessment system combining qualitative and quantitative indexes is constructed, and the comprehensive weight of the evaluation indexes is determined by combination weighting method. And combining with the scoring of evaluation indexes through quantitative analysis of point cloud, qualitative evaluation of experts, and published high-level papers, the comprehensive evaluation cloud model is constructed. According to the standard grade division, the criteria cloud model is constructed. And safety performance grade is determined by comparing the cloud similarity between the two clouds. Then this paper sets Yingxian wooden pagoda in China as an example, safety performance is quantified under this approach to determine its safety performance grade. The results show that the pagoda is the closest to third grade, in a certain dangerous state and consistent with the actual situation. The method takes into account the randomness and fuzziness factors, which makes assessment process more comprehensive and objective, provides a research paradigm for the safety assessment of ancient wooden buildings.

PD-0400 Quantitative deformable image registration uncertainty framework for proton head and neck treatment

PD-0400 Quantitative deformable image registration uncertainty framework for proton head and neck treatment

Multistep prediction of dynamic uncertainty under limited data

Engineering systems are growing in complexity, requiring increasingly intelligent and flexible methods to account for and predict uncertainties in service. This paper presents a framework for dynamic uncertainty prediction under limited data (UPLD). Spatial geometry is incorporated with LSTM networks to enable real-time multistep prediction of quantitative and qualitative uncertainty over time. Validation is achieved through two case studies. Results demonstrate robust prediction of trends in limited and dynamic uncertainty data with parallel determination of geometric symmetry at each time unit. Future work is recommended to explore alternative network architectures suited to limited data scenarios.

Towards quantifying the uncertainty in in silico predictions using Bayesian learning

Next-generation risk assessment (NGRA) involves the combination of in vitro and in silico models for more human-relevant, ethical, and sustainable human chemical safety assessment. NGRA requires a quantitative mechanistic understanding of the effects of chemicals across human biology (be they molecular, cellular, organ-level or higher) coupled with a quantitative understanding of the uncertainty in any experimentally measured or predicted values. These values with their uncertainties can then be considered as a probability distribution, which can then be compared to exposure estimates to establish the presence or absence of a margin of safety. We have constructed Bayesian learning neural networks to provide such quantitative predictions and uncertainties for 20 pharmacologically important human molecular initiating events. These models produce high quality quantitative estimates (p(IC50), p(EC50), p(Ki), p(Kd)) of biochemical activity at a molecular initiating event (MIE) with average mean absolute errors (in Log units) of 0.625 ± 0.048 in test data and 0.941 ± 0.215 in external validation data. The key advantage of these models is their ability to also produce standard deviations and credible intervals (CIs) to quantify the uncertainty in these predictions, which we show to be able to distinguish between molecules close to the training data in chemical structure, those less similar to the training data, and decoy compounds drawn from the wider ChEMBL database. These uncertainty values mean that when a prediction is made a user can understand the certainty of the prediction, similar to a quantitative applicability domain, aiding prediction usefulness in NGRA. The ability for in silico methods to produce quantitative predictions with these kinds of probability distributions will be vital to their further use in NGRA, and here clear first steps have been taken.

Risk assessment of unsafe behavior in university laboratories using the HFACS-UL and a fuzzy Bayesian network

Introduction: Risk assessment for unsafe behavior is an important task in the management of university laboratories. Yet related research activities are still in the early stages. This paper attempts to deepen the insight and provide a basis for further research. Method: As traditional methods are inadequate in terms of quantitative assessment and uncertainty handling, this paper proposes a method to assess the risk of unsafe behavior in university laboratories using the human factor analysis and classification system for university laboratories (HFACS-UL)-fuzzy Bayesian network (BN) approach. A BN structure was established using the HFACS-UL model for the identification of factors influencing unsafe behavior. Using a fuzzy BN approach, parameters are learned based on prior knowledge and expert experience. The model is then applied for inference analysis to identify the main risk factors. The key agents were also analyzed along with meta-networks to determine further preventive and control measures. Results: Taking chemistry laboratories of a university as an example, the results show that the probability of unacceptable unsafe behavior in chemical laboratories is 86%, indicating that commitment and cooperation from different agents are required. Of the 24 risk factors, poor organizational climate, with a sensitivity value of 24.1%, has the greatest impact on unsafe behavior. The most fundamental factor contributing to the occurrence of unsafe behavior is inadequate legislation, which in turn results in unacceptable external factors and inadequate supervision, thus forming the most likely causal chain. The functional department, lab center director, and secondary faculty leadership team are the most critical agents. Conclusions: Results from the chemistry laboratories demonstrate the credibility of the model. Practical applications: This study may help provide technical support for risk management in university laboratories.

An Integrated Fuzzy MCDM Hybrid Methodology to Analyze Agricultural Production

A hybrid model was developed by combining multiple-criteria decision-making (MCDM) with the analytic hierarchy process (AHP) and a fuzzy set to give decision support for choosing sustainable solutions to agricultural problems. Six steps were taken to build the suggested hybrid model: identifying and weighing criteria; normalizing data using fuzzy membership functions; calculating the weighting of the criteria using AHP; and selecting the best alternative for the agricultural problem. The objective of this case study is to demonstrate how agricultural production techniques (APTs) are becoming more complex as agricultural production becomes more complex. Organic agriculture aims to protect both the environment and consumer satisfaction by utilizing organic management practices that do not have the negative effects associated with conventional and genetic engineering production. Meanwhile, products obtained through conventional and genetic engineering techniques are more cost-effective. To present the superiority of the proposed fuzzy MCDM hybrid model, this problem is used as the causative agent’s dataset. Because the challenge involves a large number of competing quantitative and qualitative criteria, the assessment approach should improve the ratio of input data to output data. As a result, agricultural productivity should be controlled holistically. However, because the problem may contain both qualitative and quantitative facts and uncertainties, it is necessary to represent the uncertainty inherent in human thinking. To achieve superior outcomes, fuzzy set theory (FST), which enables the expression of uncertainty in human judgments, can be integrated with). The purpose of this study is to present a novel MCDM approach based on fuzzy numbers for analyzing decision-making scenarios. The proposed methodology, which is based on Buckley’s fuzzy analytic hierarchy process (B-FAHP) and the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS), uses Buckley’s fuzzy analytic hierarchy process (B-FAHP) and fuzzy TOPSIS to determine weights and rank alternatives, respectively. As a result, we attempted to include both the uncertainty and hesitancy of experts in the decision-making process through the use of fuzzy numbers. We have three main criteria in this study: Satisfaction (C1), Economy (C2), and Environment (C3). An important objective of the current research is to build a complete framework for evaluating and grading the suitability of technologies. A real-world case study is used to demonstrate the suggested paradigm’s validity.

Quantitative Uncertainty Principles Related to Lions Transform

Quantitative Uncertainty Principles Related to Lions Transform

Assessment of Fire Model Uncertainty of Fire Dynamics Simulator in Multiple Compartments of Nuclear Power Plant

It is necessary to consider the uncertainty of a fire model to ensure accuracy in fire risk assessment. Recently, NUREG-1824 reported fire model uncertainty data based on 340 full-scale fire experiment results. However, somewhat inaccurate test results can be obtained when the statistical uncertainty of the model is applied to specific fire risk assessments. In this study, the uncertainty of the fire model, Fire Dynamics Simulator (FDS), was investigated based on PRISME3 fire experiment results. Measurement and prediction results for approximately 200 locations were compared and analyzed, and the quantitative model uncertainty was calculated using the bias factor and relative standard deviation. The results showed that although the computed FDS bias factor of the heat flux based on NUREG-1824 test results was 0.89, 1.18 was obtained in this study, indicating a significant difference. Although the model uncertainties for the same fire experiment results were reviewed, a maximum difference of 12% in model uncertainty was observed, depending on the measurement location.

Forecasting the Future Sustainability of Technology Choices: Qualitative Predictive Validity of Models as a Complement to Quantitative Uncertainty

To support product and technology choices toward a more sustainable future, diverse assessment methods are used, involving life cycle assessment (LCA). This raises the question of their predictive validity. Whereas, many studies focus on quantitative uncertainty, here the main aim is to address the complementary qualitative aspect of the LCA-related model variants. To that end, we first specify three general influential aspects: (1) future conditions, (2) needed predictivity, and (3) mechanism coverage. These have been translated into a more concrete checklist for qualitative predictive validity. Second, we categorized the model variants into a limited number of basic model types, based on five predefined modeling characteristics. These model types show increasingly complex steps for investigating the future, illustrated with energy systems for transport. Different answers to the same questions may result. With increasing model complexity, the relevant questions may change from analysing specific products, to more general product systems, and next to product-technology domain systems. As a third step, the qualitative predictive validity of the nine modeling types is evaluated using the developed checklist. All have limited predictive validity, increasingly so for longer time horizons, as they lack most causal mechanisms, especially the institutional drivers for development and employment of technologies to emerge. Also, the future is only partially determined. For supporting choices, the conclusion is that the comparative analysis regarding long-term also broader product-technology systems has limited predictive validity. As a solution, conditional statements may show directions for explorative analysis resulting in highly tentative advice on potentially attractive directions.

Assessing the Ability to Quantify Bathymetric Change over Time Using Solely Satellite-Based Measurements

Coastal regions are undergoing rapid change, due to natural and anthropogenic forcings. A current constraint in understanding and modeling these changes is the lack of multi-temporal bathymetric data, or recursive observations. Often, it is difficult to obtain the repeat observations needed to quantify bathymetric change over time or events. However, the recent availability of ICESat-2 bathymetric lidar creates the option to map coastal bathymetry from solely space-based measurements via satellite-derived bathymetry with multispectral imagery (IS-2/SDB). This compositional space-based bathymetric mapping technique can assess temporal change along the coasts without other remote sensing or in situ data. However, questions exist as to the accuracy of the technique relative to both quantitative uncertainties and the ability to resolve the spatial patterns of erosion and deposition in the nearshore environment, indicative of geomorphologic change. This paper addresses the concept using data from the Florida panhandle (Northern Gulf of Mexico) collected by Sentinel-2 and ICESat-2 at two epochs to assess the feasibility of using IS-2/SDB for bathymetric change detection at scientifically relevant scales, spatial resolutions and accuracies. The comparison of the satellite-only result is compared to airborne data collected at similar epochs to reveal both quantitatively and qualitatively the utility of this technique.

Quantitative uncertainty estimation in biophysical models of fish larval connectivity in the Florida Keys

Abstract The impacts of seven uncertain biological parameters on simulated larval connectivity in the Florida Keys are investigated using Polynomial chaos surrogates. These parameters describe biological traits and behaviours—such as mortality, swimming abilities, and orientation—and modulate larval settlement as well as dispersal forecasts. However, these parameters are poorly constrained by observations and vary naturally between individual larvae. The present investigation characterizes these input uncertainties with probability density functions informed by previous studies of Abudefduf saxatilis. The parametric domain is sampled via ensemble calculations, then a polynomial-based surrogate is built to explicitly approximate the dependence of the model outputs on the uncertain model inputs, which enables a robust statistical analysis of uncertainties. This approach allows the computation of probabilistic dispersal kernels that are further analyzed to understand the impact of the parameter uncertainties. We find that the biological input parameters influence the connectivity differently depending on dispersal distance and release location. The global sensitivity analysis shows that the interactions between detection distance threshold, orientation ontogeny, and orientation accuracy, are the dominant contributors to the uncertainty in settlement abundance in the Florida Keys. Uncertainties in swimming speed and mortality, on the other hand, seem to contribute little to dispersal uncertainty.

Rapid and quantitative uncertainty estimation of coseismic slip distribution for large interplate earthquakes using real-time GNSS data and its application to tsunami inundation prediction

This study proposes a new method for the uncertainty estimation of coseismic slip distribution on the plate interface deduced from real-time global navigation satellite system (GNSS) data and explores its application for tsunami inundation prediction. Jointly developed by the Geospatial Information Authority of Japan and Tohoku University, REGARD (REal-time GEONET Analysis system for Rapid Deformation monitoring) estimates coseismic fault models (a single rectangular fault model and slip distribution model) in real time to support tsunami prediction. The estimated results are adopted as part of the Disaster Information System, which is used by the Cabinet Office of the Government of Japan to assess tsunami inundation and damage. However, the REGARD system currently struggles to estimate the quantitative uncertainty of the estimated result, although the obtained result should contain both observation and modeling errors caused by the model settings. Understanding such quantitative uncertainties based on the input data is essential for utilizing this resource for disaster response. We developed an algorithm that estimates the coseismic slip distribution and its uncertainties using Markov chain Monte Carlo methods. We focused on the Nankai Trough of southwest Japan, where megathrust earthquakes have repeatedly occurred, and used simulation data to assume a Hoei-type earthquake. We divided the 2951 rectangular subfaults on the plate interface and designed a multistage sampling flow with stepwise perturbation groups. As a result, we successfully estimated the slip distribution and its uncertainty at the 95% confidence interval of the posterior probability density function. Furthermore, we developed a new visualization procedure that shows the risk of tsunami inundation and the probability on a map. Under the algorithm, we regarded the Markov chain Monte Carlo samples as individual fault models and clustered them using the k-means approach to obtain different tsunami source scenarios. We then calculated the parallel tsunami inundations and integrated the results on the map. This map, which expresses the uncertainties of tsunami inundation caused by uncertainties in the coseismic fault estimation, offers quantitative and real time insights into possible worst-case scenarios.Graphical