Sources Of Uncertainty Research Articles

Vertical Profile Analysis of Cloud Feedbacks

AbstractCloud feedback is the largest source of uncertainty in climate sensitivity. This feedback is generally discussed in terms of radiative flux at the top of the atmosphere, but the vertical distribution of the contribution of cloud changes to the top‐of‐atmosphere cloud feedback should be discussed to understand the feedback in greater detail. We have developed a simple analysis method called “vertical profile analysis of cloud feedback,” which simply uses radiative flux data from each level of the model. The analysis is applied for typical cloud regimes and the results are discussed together with cloud fraction change profiles. The advantages and disadvantages of the vertical profile analysis, compared with the commonly used International Satellite Cloud Climatology Project (ISCCP) histogram kernel method, are discussed. Our analysis has the advantage of resolving the detailed vertical profiles of the contribution to the top‐of‐atmosphere cloud feedback from the changes in cloud regimes associated with warming, such as reduced cloud cover and upward shifts of the cloud layer in subtropical low‐cloud regions as well as the increased height of the melting layer in tropical deep convection. The main disadvantage is that the vertical profile analysis cannot represent the feedback components sorted by cloud optical thickness as in the ISCCP histogram kernel method.

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.

The Virgo Newtonian calibration system for the O4 observing run

Abstract After initial tests performed during previous observing runs, a Newtonian Calibrator (NCal) system was developed and installed on the Virgo gravitational wave detector for the O4 observing run. This system, which is continuously operated, provides the absolute calibration of Virgo for this run. Its 1-σ uncertainty of 0.17% on the amplitudes of the injected signals is better than that obtained with other calibration techniques like the photon calibrator (PCal). This paper presents this NCal system and details the different sources of uncertainties.

A novel frequency constrained unit commitment considering VSC-HVDC's frequency support in asynchronous interconnected system under renewable energy Source's uncertainty

The increasing share of renewable energy source (RES) poses a challenge to the frequency security of the power system. Frequency constrained unit commitment (FCUC) serves as an effective measure to address this challenge at the operational level. This paper introduces a novel FCUC model applicable to the asynchronous interconnected system connected by voltage source converter based HVDC (VSCHVDC), fully taking into account the frequency support capability of VSCHVDC in order to reduce the demand for synchronous generators' inertia and reserve while ensuring frequency security, thereby lowering operating costs. Additionally, the uncertainty of RES is also considered. The constraint expressions for three frequency indicators are derived based on a system frequency response model that includes frequency support from VSCHVDC. The proposed model optimizes unit commitment, generation and reserve dispatch and VSCHVDC transmission power and frequency response parameters, while addressing RES uncertainty using the distributionally robust chance constrained approach. In response to the highly nonlinear characteristics of the maximum frequency derivation (MFD) constraints, we propose an intelligent sampling-based support vector machine to convexify the MFD constraints and introduce a two-stage decomposition algorithm for solving the model. The effectiveness of the proposed model is demonstrated based on a modified IEEE RTS-79 system.

Fragility estimation for performance-based structural design of floating offshore wind turbine components

This study proposes a computational and mathematical framework aimed at assessing the reliability of structural components within Floating Offshore Wind Turbines (FOWT) that reflects the various sources of uncertainties coupled between structural analyses, hydrodynamics, and aerodynamics. The limit state functions are represented through structural capacity and environmental demand models for selected structural failure modes that incorporate fully coupled aero-hydro-servo-elastic analysis. The fragility surfaces are developed for a selected benchmark wind turbine for both operating and parking conditions. The fragilities are also estimated under 50-year and 100-year environmental conditions in the selected U.S. coastal regions. It is found that the wind speed variations largely affect the fragility during non-operation, while wave height variations are significant during operation. Increased uncertainties in environmental parameters raised failure probabilities, especially in lower fragility ranges targeted by design codes. Analyses in U.S. coastal environments show both parking and operating conditions can be critical, challenging the previous focus on parking. Sensitivity studies reveal that under mild conditions, structural reliability is influenced by moment of inertia and material strength, but as environmental loads increase, these parameters become equally significant. Increased uncertainties in parameters lead to higher failure risks, especially below 25 m/s wind speeds.

Stochastic optimization of a mass-in-mass cell with piecewise hybrid nonlinear–linear restoring force

This article investigates the optimization under uncertainty of a mass-in-mass meta-cell for its potential use within a metamaterial. The specificity of the proposed mass-in-mass system stems from the hybrid nonlinear–linear stiffness at the inner level. It is well known that these systems are highly sensitivity to small perturbations in loading conditions or design parameters. In fact, the sensitivity is such that the system can exhibit discontinuous behaviors. Therefore the proposed optimization approach not only accounts for sources of uncertainties but also can handle discontinuous responses. The objective of the stochastic optimization is to find the stiffness properties of the mass-in-mass system which minimize the expected value of a specific efficiency metric. In order to better understand the system’s dynamic behavior and the origins of the discontinuities, slow invariant manifolds and frequency response curves are provided. The efficiency of the optimized system with hybrid stiffness is compared with that of a similar optimized system featuring pure cubic nonlinearity.

Analysis of implicit and explicit uncertainties in QSAR prediction of chemical toxicity: A case study of neurotoxicity

Although uncertainties expressed in texts within QSAR studies can guide quantitative uncertainty estimations, they are often overlooked during uncertainty analysis. Using neurotoxicity as an example, this study developed a method to support analysis of implicitly and explicitly expressed uncertainties in QSAR modeling studies. Text content analysis was employed to identify implicit and explicit uncertainty indicators, whereafter uncertainties within the indicator-containing sentences were identified and systematically categorized according to 20 uncertainty sources. Our results show that implicit uncertainty was more frequent within most uncertainty sources (13/20), while explicit uncertainty was more frequent in only three sources, indicating that uncertainty is predominantly expressed implicitly in the field. The most highly cited sources included Mechanistic plausibility, Model relevance and Model performance, suggesting they constitute sources of most concern. The fact that other sources like Data balance were not mentioned, although it is recognized in the broader QSAR literature as an area of concern, demonstrates that the output from the type of analysis conducted here must be interpreted in the context of the broader QSAR literature before conclusions are drawn. Overall, the method established here can be applied in other QSAR modeling contexts and ultimately guide efforts targeted towards addressing the identified uncertainty sources.

Reverse logistics for electric vehicles under uncertainty: An intelligent emergency management approach

The frequency and intensity of global disasters, including the COVID-19 pandemic, and natural disasters such as earthquakes, floods, and wildfires, are increasing, necessitating effective emergency logistics management. Climate change significantly contributes to these events, emphasizing the importance of limiting human and environmental impacts. The transportation sector, particularly the automobile industry, ranks second in global carbon emissions, highlighting the need to adopt electric vehicles (EVs) to reduce emissions and minimize the impact of climate change. However, this has led to an increase in demand for lithium-ion batteries. During emergencies, end-of-life (EOL) battery management through reverse logistics is essential because recycling EOL batteries can recover valuable raw materials, decrease landfill waste and costs, and support environmental sustainability. This study proposed a two-phase method for intelligent emergency EV battery reverse logistics management. The first phase employed machine learning to address unpredictable battery demands, whereas the second phase proposed a multi-objective model to minimize carbon emissions through efficient order allocation during uncertain emergencies. The model considers carbon emissions and defect rates as sources of uncertainty, current regulations, and customer environmental awareness. The model is solved using the weighted sum and ε-constraint methods, resulting in non-dominant solutions. The findings indicate that combining the selection of third-party reverse logistics providers (3PRLPs) with optimal order allocation for recycling old batteries during emergencies effectively minimizes environmental impacts and combats climate change.

Marine clay maturation induces systematic silicon isotope decrease in authigenic clays and pore fluids

Marine silicate alteration exerts a major influence on marine carbon and cation cycles, but has proven difficult to quantify. In this context, silicon isotopes of marine pore fluids became an important tracer. However, poorly constrained silicon isotope signatures of precipitates produced during silicate alteration (i.e. authigenic clays) remain a major source of uncertainty. Here we present in situ silicon isotope analyses of marine authigenic clays (intergrown iron-smectites and iron-glauconites) occurring within recent sediments from the Oregon margin, eastern North Pacific. We identify a trend to lower silicon isotopes (from −2.24‰ to −3.17‰), accompanied by decreasing aluminum/silicon ratios and increasing potassium oxide contents, which we interpret as an isotopic shift caused by progressive clay maturation via dissolution-reprecipitation reactions. Our modelling suggests that this clay maturation pathway, together with mixing of other fluid sources, may induce pore fluid silicon isotope shifts of up to −1.7‰, if sufficient newly precipitated clays are re-dissolved. This could potentially produce silicon isotopes values significantly lower than seawater and implies that conventional isotope-based approaches underestimate the prevalence of marine silicate alteration. Our findings highlight that clay maturation must be considered when interpreting silicon isotope signatures in terms of marine silicate alteration and upscaling to global element cycles.

Uncertainty quantification of collective nuclear observables from the chiral potential parametrization

Abstract We perform an uncertainty estimate of quadrupole moments and B(E2) transition rates that inform nuclear collectivity. In particular, we study the low-lying states of 6Li and 12C using the ab initio symmetry-adapted no-core shell model. For a narrow standard deviation of approximately 1% on the low-energy constants which parametrize high-precision chiral potentials, we find output standard deviations in the collective observables ranging from approximately 3-6%. The results mark the first step towards a rigorous uncertainty quantification of collectivity in nuclei that aims to account for all sources of uncertainty in ab initio descriptions of challenging collective and clustering observables.

Attribution of Excess Methane Emissions Over Marine Environments of the Mediterranean and Arabian Peninsula

AbstractTo accurately assess the current atmospheric methane budget and its future trends, it is essential to apportion and quantify the anthropogenic methane emissions to specific sources. This poses a significant challenge in the under‐sampled Middle East, where estimates predominantly depend on remote sensing observations and bottom‐up reporting of national emissions. Here, we present in situ shipborne observations of greenhouse gases (GHGs) and non‐methane hydrocarbons (NMHCs) collected along a >10,000‐km route from Vigo, Spain, to Abu Dhabi, UAE. By comparing our observations with Lagrangian dispersion model simulations, coupled with two methane emission inventories, we identify periods of considerable mismatch and apportion the responsible sources. Employing interspecies relationships with NMHCs has enabled the characterization of methane emissions from oil and gas (O&G) operations, urban centers, Red Sea deep water, enteric fermentation, and agriculture across diverse atmospheric environments. Our analysis reveals that the Suez area is a regional emission hotspot, where simulations consistently underestimate the methane emission sources. Importantly, the Middle Eastern O&G sector has been identified as an additional source of considerable uncertainty. Here, methane emissions were alternately underestimated and overestimated by the two inventories, exposing significant gaps in our understanding of fuel exploitation‐related emissions in the Middle East. This underscores the need for further targeted field campaigns and long‐term observations to improve the accuracy of emission data in the inventories.

Evaluation and projection of changes in temperature and precipitation over Northwest China based on CMIP6 models

AbstractNorthwest China is much more sensitive to climate warming, and the climate has varied rapidly from warm and drought to warm and humid conditions. In addition, due to the complex terrain of Northwest China, the methods and parameterization schemes of different CMIP6 models, these models are mostly applied to arid areas in Northwest China or Central Asia, lacking climate data for plateau areas and eastern Lanzhou, specifically in filtering CMIP6 models and evaluating applicable models. In this paper, 34 CMIP6 climate models are used to evaluate and forecast future trends in Northwest China under the SSP126, SSP245 and SSP585 scenarios in the short, medium and long term. CMIP6 models of temperature and precipitation are identified by applying the interannual variability skill score (IVS) between CN05.1 datasets and historical CMIP6 models, which are suitable for Northwest China. Then, we assess the characteristics, warming and wetting deviations, and uncertainties in the prediction of climatic change according to CMIP6 models over Northwest China. The results show that CMIP6 models in precipitation and temperature applicable to Northwest China are AWI‐CM‐1‐1‐MR, BCC‐CSM2‐MR, FGOALS‐g3, INM‐CM4‐8, INM‐CM5‐0 and MRI‐ESM2‐0. The multi‐model ensemble mean (MMEM) has better capability than individual CMIP6 models in precipitation and temperature prediction. Spatiotemporal climatic change over Northwest China shows overall warming and wetting trends. The IVS provides the ability to estimate CMIP6 model simulation performance both temporally and spatially. The temperature simulation is quite good in the Tarim Basin and Hexi Corridor region, and the precipitation simulation is quite good in the plateau region, Altai Mountains, Tianshan Mountains and Hexi Corridor region. Cold and wet deviations occur in Northwest China due to the topography and few stations, which are common reasons. The main sources of uncertainties in temperature prediction during this century are model uncertainty (before the 2090s) and scenario variability (after the 2090s), and model uncertainty in precipitation for CMIP6 becomes the main source of uncertainty.

On application of the relative entropy concept in reliability assessment of some engineering cable structures

The main research problem studied in this work is an uncertain response and reliability assessment of the spatial cable structures due to the environmental stochasticity as well as material and geometrical imperfections. Some popular cable structures are analyzed for this purpose using the Stochastic Finite Element Method (SFEM) implemented with the use of three different techniques, namely the iterative generalized perturbation method, semi-analytical approach as well as the Monte-Carlo simulation. Uncertainty quantification delivered in this study is based on the series of FEM analyses of both static and dynamic structural problems. They enable the Least Squares Method determination of the structural polynomial responses linking extreme stresses and deformations with several uncorrelated uncertainty sources. Reliability assessment, fundamental in durability and Structural Health Monitoring, is completed using a comparison of the First Order Reliability Method (FORM) with probabilistic distance formulated by Bhattacharyya. Input uncertainties are assumed to be Gaussian according to the Maximum Entropy Principle. They have specific expected values following engineering design demands or the provisions of designing codes, whereas their standard deviations do not exceed the 10% level. The methods presented and the results obtained in this study may serve for further reliability analyses of large-scale civil engineering structures completed with both steel cables and also reinforced concrete plates like suspended bridges, for instance.

A “rotating menu” of medical uncertainty for families affected by telomere biology disorders: A qualitative interview study

BackgroundMedical uncertainty may cause distress and challenge medical decision-making for patients with rare diseases and their caregivers. Few studies have examined the experience and management of medical uncertainty in rare disease and the dynamics of multiple medical uncertainty sources, issues, and management strategies. ObjectiveWe explored the experience and management of uncertainty in individuals with telomere biology disorders (TBDs), a set of rare cancer-prone bone marrow failure syndromes, and their caregivers. DesignParticipants (N = 32) in this qualitative-descriptive study were individuals with a TBD (n = 17) and/or their caregivers (n = 15). We thematically analyzed transcripts to describe the presence and dynamics of medical uncertainty in TBDs using categories from a previously published taxonomy. ResultsIndividuals with TBDs and caregivers described medical uncertainty as a chronic burden embodied amidst a range of interrelated sources and issues. Scientific uncertainty included diagnostic and prognostic ambiguity. Practical uncertainty focused on logistical challenges of building and maintaining medical care teams. Personal uncertainty included difficulty realigning self-identity, goals, and relationship expectations post-diagnosis. Scientific, practical, and personal uncertainty issues were entangled. The rarity of TBDs resulted in limited scientific knowledge, which gave rise to practical and personal uncertainties affecting medical decision-making and relationship formation (e.g., creating trusted care teams where patient knowledge of TBDs may exceed that of clinicians). Participants used multiple strategies for uncertainty management, particularly information-seeking and community-building. However, these management strategies could intensify, rather than resolve, participants’ medical uncertainty. ConclusionIn TBDs, medical uncertainty manifests as a network of multiple, interrelated, sources and issues, which require evolving management strategies. Researchers must be mindful that complex, synergistic uncertainty networks contribute to psychosocial challenges in TBDs. Additional research is warranted to address scientific uncertainty in TBDs, including clinical manifestations and underlying biology, and to develop psychosocial interventions that recognize and anticipate evolving uncertainty.

Exploring the Relationship Between Gene Expression and Low-Frequency Somatic Mutations in Arabidopsis with Duplex Sequencing.

Intragenomic mutation rates can vary dramatically due to transcription-associated mutagenesis or transcription-coupled repair, which vary based on local epigenomic modifications that are nonuniformly distributed across genomes. One feature associated with decreased mutation is higher expression level, which depends on environmental cues. To understand the magnitude of expression-dependent mutation rate variation, we perturbed expression through a heat treatment in Arabidopsis thaliana. We quantified gene expression to identify differentially expressed genes, which we then targeted for mutation detection using duplex sequencing. This approach provided a highly accurate measurement of the frequency of rare somatic mutations in vegetative plant tissues, which has been a recent source of uncertainty. Somatic mutations in plants may be useful for understanding drivers of DNA damage and repair in the germline since plants experience late germline segregation and both somatic and germline cells share common repair machinery. We included mutant lines lacking mismatch repair (MMR) and base excision repair (BER) capabilities to understand how repair mechanisms may drive biased mutation accumulation. We found wild-type (WT) and BER mutant mutation frequencies to be very low (mean variant frequency 1.8 × 10-8 and 2.6 × 10-8, respectively), while MMR mutant frequencies were significantly elevated (1.13 × 10-6). Interestingly, in the MMR mutant lines, there was no difference in the somatic mutation frequencies between temperature treatments or between highly versus lowly expressed genes. The extremely low somatic variant frequencies in WT plants indicate that larger datasets will be needed to address fundamental evolutionary questions about whether environmental change leads to gene-specific changes in mutation rate.

Microgrid control under uncertainty

Microgrids – decentralized electrical grids that can function both in conjunction with wide area macrogrids and without – are a powerful tool to address energy resiliency and climate change mitigation. Microgrid control, however, remains a challenge; their bespoke nature and the existence of multiple sources of uncertainty lead to a control problem that traditional grid modeling and control techniques are ill-suited to handle. We build a microgrid interface to simulate microgrids under uncertainty and devise off-policy reinforcement learning algorithms to control microgrids. Our algorithms, which incorporate domain randomization and random network distillation for exploration and computational efficiency, achieve performance better than model predictive control and rule based control benchmarks under battery model uncertainty on seven of ten tested scenarios. Our model code is available at https://github.com/ahalev/Microgrid-Control-Under-Uncertainty and our microgrid simulator is available at https://github.com/ahalev/python-microgrid.

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

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

Distribution-free prediction intervals with conformal prediction for acoustical estimation.

Acoustical parameter estimation is a routine task in many domains. The performance of existing estimation methods is affected by external uncertainty, yet the methods provide no measure of confidence in the estimates. Hence, it is crucial to quantify estimate uncertainty before real-world deployment. Conformal prediction (CP) generates statistically valid prediction intervals for any estimation model using calibration data; a limitation is that calibration data needed by CP must come from the same distribution as the test-time data. In this work, we propose to use CP to obtain statistically valid uncertainty intervals for acoustical parameter estimation using a data-driven model or an analytical model without training data. We consider direction-of-arrival estimation and localization of sources. The performance is validated on plane wave data with different sources of uncertainty, including ambient noise, interference, and sensor location uncertainty. The application of CP for data-driven and traditional propagation models is demonstrated. Results show that CP can be used for statistically valid uncertainty quantification with proper calibration data.

Incorporating multi-source uncertainties in fast building wall thermal resistance estimation through physics-based and multi-fidelity statistical learning models

Meta-modeling and Bayesian inversion technique are proposed for fast and accurate in-situ estimation of the total thermal resistance (RTot) of walls using non-intrusive wall instrumentation. Various sources of uncertainties are taken into account to provide an enhanced credible interval for the thermal resistance estimate. In the considered protocol, a small zone of the internal surface of the wall is excited by a prototype to get faster in situ estimation and to limit the influence of external weather conditions. To be independent of the heat transfer coefficients and of the misknown layer thicknesses, which are difficult to estimate, we use herein the measurements of internal and external surface temperatures as boundary conditions in a thermal direct problem reformulated. A meta-model of the thermal model is created based on a statistical multi-fidelity approach with two levels of fidelity, Resistance-Capacitance (RC) and 1D models, to achieve the Bayesian estimation of the total thermal resistance in a reasonable computation time. The RTot estimation method is applied to realistic internal insulated walls (IIW), from poorly to highly insulated, under different weather conditions. Several numerical tests are carried out and credible intervals are provided to study the importance of the wall initial condition, the excitation time and the instrumentation. Finally, an experimental application is conducted using real measurements on an internal insulated wall (IIW) in Nancy (France). The obtained experimental results show that, in a short excitation time (10h) with a reduced instrumentation, the proposed method accurately estimates the minimum wall thermal resistance. For a standard wall of about 4 m2.K/W, a relevant estimate of RTot with a relative deviation less than 10% can be achieved by using a polynomial initial temperature profile proposed in this study, sufficient measurements and an excitation time of 3 days. This study thus offers prospects for improved energy assessment of buildings before and after renovation.

Interpreting and evaluating digital soil mapping prediction uncertainty: A case study using texture from SoilGrids

Soil information is critical for a wide range of land resource and environmental decisions. These decisions will be compromised when the soil information quality is unsatisfactory. Thus, users of soil information need to understand and consider the uncertainty of the available soil information and be able to judge whether it is fit for purpose. The uncertainty information provided with the SoilGrids2.0 product was examined in a case study. We hypothesised that the soil property predictions for the Netherlands (NL) might be less uncertain than those of New Zealand (NZ) because there were more relevant training data for NL than for NZ. The study objectives were to: 1)understand whether the provided uncertainty information is correct for both countries; 2)explore spatial patterns and relationships in the prediction error and uncertainty information using quantitative tools and new graphical analyses; 3)analyse whether these patterns and relations can be explained; and 4)explore how the uncertainty information and insights derived from graphical analyses might assist an end user to determine whether a map is suitable for their purpose. The study focused on soil texture.Independent datasets showed that the SoilGrids 2.0 uncertainty information was too optimistic for sand and too pessimistic for clay for both countries. The graphical analyses confirmed the initial assumption that NL predictions were more accurate than those for NZ, but they also indicated that some locations in NL have high uncertainty. The graphical analyses allowed only a limited identification of the four sources of uncertainty in digital soil maps, but were quite insightful in helping us to better understand the reliability of the information. A set of recommendations was developed for both producers and consumers of digital soil mapping (DSM) products. This includes the provision of a summary map of accuracy classes. We suggest that more research and educational effort is needed to ensure that digital soil maps are used appropriately.