Uncertainty In Extrapolation Research Articles

Detailed multivariate comparisons of environments with mobility oriented parity

Multivariate model projections onto conditions that differ from those under which the models were calibrated can result in uncertainty owing to response extrapolation. Therefore, interpretations of models transferred to conditions not analogous to those of model calibration must be done carefully to avoid misleading conclusions. A good practice when producing model transfers is to assess the degree of dissimilarity between calibration and transfer conditions. The mobility oriented parity (MOP) metric is a tool commonly used to quantify such dissimilarities. Our study elucidated the details of MOP, and expanded the interpretability of results when transfer conditions are not analogous to those of model calibration. We implemented a more detailed characterization of non-analogous conditions previously only identified as outside calibration ranges. In addition, we quantified the sensitivity of MOP to the density and position of reference conditions in environmental space, and explored the effect of sample size on MOP results. We show that detailed characterizations of non-analogous conditions can help to determine which variables are different between the two areas and how. This information can be used to understand causes of environmental dissimilarity and to identify variables that could be excluded from ecological niche modeling owing to extrapolation risks associated with them. Our results also show that dissimilarity values calculated in comparisons offer good indicators of how reliable interpretations can be in areas with conditions not analogous to those of reference. The tools developed and implemented in this study provide a useful, quantitatively efficient means of characterizing extrapolation uncertainty in model transfer studies, a best practice when using multivariate ecological niche models to make predictions about species’ distributions. An extended version of the mobility oriented parity metric helps identify variables for which risks of model extrapolations can be expected. Distinct MOP metrics used to characterize dissimilarity show comparable general patterns in transfer areas, despite differences in values. As MOP results are influenced by background sampling size, the definition of this sample should be aimed to appropriately characterize calibration areas.

Optimal Mapping of Soil Erodibility in a Plateau Lake Watershed: Empirical Models Empowered by Machine Learning

Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R2 of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion.

Nominal state determination and its effect on remaining useful life prediction

Abstract In machinery operation, a prolonged healthy or nominal state often lacks prognostic significance, causing challenges like data overload, biased predictions, and complex models. Moreover, many prediction methods utilize the complete history of monitoring data from the machine’s startup to its failure; however, prognostics mostly relies on data from the degradation stage. To address this, this study proposes a method to identify and exclude the prolonged period of the nominal state, thereby enhancing the prediction performance of remaining useful life (RUL). A health index (HI) is formulated by integrating acceleration signals from multiple time windows, with deviations computed as the disparity between the HI and its root mean squares. The identification of start and end times for the nominal state, determined by the intersection of consecutive deviation curves, leads to its exclusion from degradation behaviour modelling. The utilization of polynomial degradation trends from HI data after the nominal state’s end time, incorporating a positive slope constraint, aids in mitigating extrapolation uncertainty. The method’s efficiency is demonstrated in three defect cases, highlighting improved RUL predictions without the nominal state’s inclusion.

IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning

We propose a novel framework for uncertainty quantification via information bottleneck (IB-UQ) for scientific machine learning tasks, including deep neural network (DNN) regression and neural operator learning (DeepONet). Specifically, we incorporate the bottleneck by a confidence-aware encoder, which encodes inputs into latent representations according to the confidence of the input data belonging to the region where training data is located, and utilize a Gaussian decoder to predict means and variances of outputs conditional on representation variables. Furthermore, we propose a data augmentation based information bottleneck objective which can enhance the quantification quality of the extrapolation uncertainty, and the encoder and decoder can be both trained by minimizing a tractable variational bound of the objective. In comparison to uncertainty quantification (UQ) methods for scientific learning tasks that rely on Bayesian neural networks with Hamiltonian Monte Carlo posterior estimators, the model we propose is computationally efficient, particularly when dealing with large-scale datasets. The effectiveness of the IB-UQ model has been demonstrated through several representative examples, such as regression for discontinuous functions, learning nonlinear operators for partial differential equations, and a large-scale climate map model. The experimental results indicate that the IB-UQ model can handle noisy data, generate robust predictions, and provide confident uncertainty evaluation for out-of-distribution data.

A simulation study of extrapolation uncertainty in exposure assessment – Use of pilot study results for site investigation

Accurate characterization of soil contaminant concentrations is often crucial for assessing risks to human and ecological health. However, fine-scale assessments of large tracts of land can be cost prohibitive due to the number of samples needed. One solution to this problem is to extrapolate sampling results from one area to another unsampled area. In the absence of a validated extrapolation methodology, regulatory agencies have employed policy-based techniques for large sites, but the likelihood of decision errors resulting from these extrapolations is largely unexplored. This study describes the results of a simulation study aimed at guiding environmental sampling for sites where extrapolation concepts are of interest. The objective of this study is to provide practical recommendations to regulatory agencies for extrapolating sampling results on large tracts of land while minimizing errors that are detrimental to human health. A variety of site investigation scenarios representative of environmental conditions and sampling schemes were tested using adaptive sampling when collecting discrete samples or applying incremental sampling methodology (ISM). These simulations address extrapolation uncertainty in cases where a Pilot Study might result in either false noncompliance or false compliance conclusions. A wide range of plausible scenarios were used that reflect the variety of heterogeneity seen at large sites. This simulation study demonstrates that ISM can be reliably applied in a Pilot Study for purposes of extrapolating the outcome to a large area site because it decreases the likelihood of false non-compliance errors while also providing reliable estimates of true compliance across unsampled areas. The results demonstrate how errors depend on the magnitude of the 95% upper confidence limit for the mean concentration (95UCL) relative to the applicable action level, and that error rates are highest when the 95UCL is within 10%–40% of the action level. The false compliance rate can be reduced to less than 5% when 30% or more of the site is characterized with ISM. False compliance error rates using ISM are insensitive to the fraction of the decision units (DUs) that are characterized with three replicates (with a minimum of 10 percent), so long as 95UCLs are calculated for the DUs with one replicate using the average coefficient of variation from the three replicate DUs.

The potential cost‐effectiveness of roflumilast drug treatment in mild cognitive impairment

AbstractBackgroundEvidence is needed on the value of new (pharmacological) health technologies in their early stage of development, to support stakeholders (industry, regulators, payers, and health care providers) in decision‐making linked to their further development and introduction in routine care. Early health‐technology assessment supports stakeholders by providing insight into the size and uncertainty of the expected benefits new technologies may have. This can facilitate early guidance on positioning in clinical practice and research recommendations. Our objective was to provide a pragmatic example of the potential cost‐effectiveness of roflumilast treatment in addition to usual care versus usual care alone if prevention of progression to dementia is in part realized in a population of patients diagnosed with a mild cognitive impairment (MCI) at a memory clinic.MethodThe IPECAD open‐source decision‐analytic model was used to simulate the natural disease progression of MCI and dementia and estimate the quality‐adjusted life years (QALYs) and care costs, comparing roflumilast treatment added to usual care with usual care alone. In this model roflumilast proof of concept evidence on a visual 30‐word verbal learning test (0.39 z‐score) was translated using a published risk equation model to a relative risk of 0.93 for developing dementia. The treatment was assumed symptomatic with no effect on mortality.ResultMore lifeyears were spent in MCI in the roflumilast strategy compared with usual care, with 0.2 increment lifeyears (95%CI: 0.0 to 0.6) (see figure 1), and 0.04 incremental lifetime QALYs (95%CI: 0.00 to 0.11) and k€1.3 adjusted societal costs (95%CI: ‐6.3 to 5.5) (see figure 2). This resulted in an incremental cost‐effectiveness ratio of k€34 per QALY (see table 1). Roflumilast was estimated to have a 37% probability of being cost‐effective (net health benefit 95% interval ‐0.27 to 0.42), with uncertainty mainly driven by extrapolating the word learning surrogate outcome to the patient‐relevant outcome of dementia onset, and the efficacy estimate (see figure 3).ConclusionThe potential cost‐effectiveness of roflumilast treatment is uncertain, driven by uncertainty in treatment efficacy and extrapolation to postponing dementia onset. We believe the results indicate a potential for roflumilast and provide several recommendations to consider investing in its future research.

Extrapolation of Survival Data Using a Bayesian Approach: A Case Study Leveraging External Data from Cilta-Cel Therapy in Multiple Myeloma.

Extrapolating long-term overall survival (OS) from shorter-term clinical trial data is key to health technology assessment in oncology. However, extrapolation using conventional methods is often subject to uncertainty. Using ciltacabtagene autoleucel (cilta-cel), a chimeric antigen receptor T-cell therapy for multiple myeloma, we used a flexible Bayesian approach to demonstrate use of external longer-term data to reduce the uncertainty in long-term extrapolation. The pivotal CARTITUDE-1 trial (NCT03548207) provided the primary efficacy data for cilta-cel, including a 12-month median follow-up snapshot of OS. Longer-term (48-month median follow-up) survival data from the phase I LEGEND-2 study (NCT03090659) were also available. Twelve-month CARTITUDE-1 OS data were extrapolated in two ways: (1) conventional survival models with standard parametric distributions (uninformed), and (2) Bayesian survival models whose shape prior was informed from 48-month LEGEND-2 data. For validation, extrapolations from 12-month CARTITUDE-1 data were compared with observed 28-month CARTITUDE-1 data. Extrapolations of the 12-month CARTITUDE-1 data using conventional uninformed parametric models were highly variable. Using informative priors from the 48-month LEGEND-2 dataset, the ranges of projected OS at different timepoints were consistently narrower. Area differences between the extrapolation curves and the 28-month CARTITUDE-1 data were generally lower in informed Bayesian models, except for the uninformed log-normal model, which had the lowest difference. Informed Bayesian survival models reduced variation of long-term projections and provided similar projections as the uninformed log-normal model. Bayesian models generated a narrower and more plausible range of OS projections from 12-month data that aligned with observed 28-month data. CARTITUDE-1 ClinicalTrials.gov identifier, NCT03548207. LEGEND-2 ClinicalTrials.gov identifier, NCT03090659, registered retrospectively on 27 March 2017, and ChiCTR-ONH-17012285.

Assessment of neurotransmitter release in human iPSC-derived neuronal/glial cells: a missing in vitro assay for regulatory developmental neurotoxicity testing.

Human induced pluripotent stem cell (hiPSC)-derived neural stem cells (NSCs) and their differentiated neuronal/glial derivatives have been recently considered suitable to assess in vitro developmental neurotoxicity (DNT) triggered by exposure to environmental chemicals. The use of human-relevant test systems combined with in vitro assays specific for different neurodevelopmental events, enables a mechanistic understanding of the possible impact of environmental chemicals on the developing brain, avoiding extrapolation uncertainties associated with in vivo studies. Currently proposed in vitro battery for regulatory DNT testing accounts for several assays suitable to study key neurodevelopmental processes, including NSC proliferation and apoptosis, differentiation into neurons and glia, neuronal migration, synaptogenesis, and neuronal network formation. However, assays suitable to measure interference of compounds with neurotransmitter release or clearance are at present not included, which represents a clear gap of the biological applicability domain of such a testing battery. Here we applied a HPLC-based methodology to measure the release of neurotransmitters in a previously characterized hiPSC-derived NSC model undergoing differentiation towards neurons and glia. Glutamate release was assessed in control cultures and upon depolarization, as well as in cultures repeatedly exposed to some known neurotoxicants (BDE47 and lead) and chemical mixtures. Obtained data indicate that these cells have the ability to release glutamate in a vesicular manner, and that both glutamate clearance and vesicular release concur in the maintenance of extracellular glutamate levels. In conclusion, analysis of neurotransmitter release is a sensitive readout that should be included in the envisioned battery of in vitro assays for DNT testing.

Extrapolating from experiments, confidently

Extrapolating causal effects from experiments to novel populations is a common practice in evidence-based-policy, development economics and other social science areas. Drawing on experimental evidence of policy effectiveness, analysts aim to predict the effects of policies in new populations, which might differ importantly from experimental populations. Existing approaches made progress in articulating the sorts of similarities one needs to assume to enable such inferences. It is also recognized, however, that many of these assumptions will remain surrounded by significant uncertainty in practice. Unfortunately, the existing literature says little on how analysts may articulate and manage these uncertainties. This paper aims to make progress on these issues. First, it considers several existing ideas that bear on issues of uncertainty, elaborates the challenges they face, and extracts some useful rationales. Second, it outlines a novel approach, called the support graph approach, that builds on these rationales and allows analysts to articulate and manage uncertainty in extrapolation in a systematic and unified way.

Towards a stochastic inverse Finite Element Method: A Gaussian Process strain extrapolation

The inverse Finite Element Method (iFEM) employing a network of strain sensors reconstructs the full-field displacement on beam or shell structures, independently of the loading conditions and of the material properties. However, the iFEM in principle requires triaxial strain measurements for each inverse element, which is practically hardly possible due to space and cost constraints. To relieve this issue, some strain values fed as input to the iFEM are typically computed using strain pre-extrapolation/interpolation techniques, and the iFEM solution is computed minimizing a weighted functional: elements missing experimental measurements are assigned low weights, which are generally set to arbitrarily low values taken from the literature. This paper proposes the use of a Gaussian Process as a strain pre-extrapolation and interpolation technique, which natively provides the extrapolation uncertainty, which in turn is used as a metric to assign the functional weights, and it enables the computation of the uncertainty on the reconstructed displacement field. The proposed approach is tested on a virtual and an experimental case study; advantages and limitations of the proposed technique are discussed.

The reduction in laminar burning velocity and Markstein length extrapolation uncertainty for TRF-air mixtures

The laminar burning velocity and Markstein length of TRF (n-heptane, isooctane, and toluene)-air mixtures were investigated at the initial temperature of 400 K and 453 K, initial pressure of 1 bar, and an equivalence ratio range of 0.8–1.5. The effects of extrapolation radius (Rf) and Markstein length (Lb) were discussed on the linear and nonlinear extrapolation models. The results showed that the linear model leads to higher extrapolation result for all equivalence ratios compared with the nonlinear model due to the differences in the third term of Taylor expansion. Finally, the laminar burning velocity and Markstein length uncertainty caused by the extrapolation model can be minimized by adjusting LbRf1 (Rf1 refers to the flame initial extrapolated radius). When the value of LbRf1 was below 0.012, the laminar burning velocity and Markstein length uncertainty can be controlled at < 0.05 and < 0.5, respectively.

Improving models to predict holocellulose and Klason lignin contents for peat soil organic matter with mid-infrared spectra

Abstract. To understand global soil organic matter (SOM) chemistry and its dynamics, we need tools to efficiently quantify SOM properties, for example, prediction models using mid-infrared spectra. However, the advantages of such models rely on their validity and accuracy. Recently, Hodgkins et al. (2018) developed models to quantitatively predict peat holocellulose and Klason lignin contents, two indicators of SOM stability and major fractions of organic matter. The models may help to understand large-scale SOM gradients and have been used in various studies. A research gap to fill is that these models have not been validated in detail yet. What are their limitations and how can we improve them? This study provides a validation with the aim to identify concrete steps to improve these models. As a first step, we provide several improvements using the original training data. The major limitation we identified is that the original training data are not representative for a range of diverse peat samples. This causes both biased estimates and extrapolation uncertainty under the original models. In addition, the original models can in practice produce unrealistic predictions (negative values or values &gt;100 mass-%). Our improved models partly reduce the observed bias, have a better predictive performance for the training data, and avoid such unrealistic predictions. Finally, we provide a proof of concept that holocellulose contents can also be predicted for mineral-rich samples (e.g., peat with mineral admixtures or potentially mineral soils). A key step to improve the models will be to collect training data that are representative for SOM formed under various conditions. This study opens directions to develop operational models to predict SOM holocellulose and Klason lignin contents from mid-infrared spectra.

Reanalysis of Trichloroethylene and Tetrachloroethylene Metabolism to Glutathione Conjugates Using Human, Rat, and Mouse Liver in Vitro Models to Improve Precision in Risk Characterization.

Both trichloroethylene (TCE) and tetrachloroethylene (PCE) are high-priority chemicals subject to numerous human health risk evaluations by a range of agencies. Metabolism of TCE and PCE determines their ultimate toxicity; important uncertainties exist in quantitative characterization of metabolism to genotoxic moieties through glutathione (GSH) conjugation and species differences therein. This study aimed to address these uncertainties using novel in vitro liver models, interspecies comparison, and a sensitive assay for quantification of GSH conjugates of TCE and PCE, S-(1,2-dichlorovinyl)glutathione (DCVG) and S-(1,2,2-trichlorovinyl) glutathione (TCVG), respectively. Liver in vitro models used herein were suspension, 2-D culture, and micropatterned coculture (MPCC) with primary human, rat, and mouse hepatocytes, as well as human induced pluripotent stem cell (iPSC)-derived hepatocytes (iHep). We found that, although efficiency of metabolism varied among models, consistent with known differences in their metabolic capacity, formation rates of DCVG and TCVG generally followed the patterns , and primary . Data derived from MPCC were most consistent with estimates from physiologically based pharmacokinetic models calibrated to invivo data. For TCE, the new data provided additional empirical support for inclusion of GSH conjugation-mediated kidney effects as critical for the derivation of noncancer toxicity values. For PCE, the data reduced previous uncertainties regarding the extent of TCVG formation in humans; this information was used to update several candidate kidney-specific noncancer toxicity values. Overall, MPCC-derived data provided physiologically relevant estimates of GSH-mediated metabolism of TCE and PCE to reduce uncertainties in interspecies extrapolation that constrained previous risk evaluations, thereby increasing the precision of risk characterizations of these high-priority toxicants. https://doi.org/10.1289/EHP12006.

Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: Extrapolation and prediction uncertainty

We use a convolutional, recurrent neural network approach to learn morphological evolution driven by surface diffusion. To this aim we first produce a training set using phase field simulations. Intentionally, we insert in such a set only relatively simple, isolated shapes. After proper data augmentation, training and validation, the model is shown to correctly predict also the evolution of previously unobserved morphologies and to have learned the correct scaling of the evolution time with size. Importantly, we quantify prediction uncertainties based on a bootstrap-aggregation procedure. The latter proved to be fundamental in pointing out high uncertainties when applying the model to more complex initial conditions (e.g., leading to the splitting of high aspect-ratio individual structures). The automatic smart augmentation of the training set and design of a hybrid simulation method are discussed.

Regional assessment of extreme significant wave heights in the Bohai Sea and northern Yellow Sea

The regional assessment of extreme significant wave heights (SWHs) is crucial for the design of coastal and offshore structures. In this study, the Bohai Sea and northern Yellow Sea are selected as the study region, and a 40-year (1979-2018) time series hindcast of SWHs is selected as the initial database. To extract the independent and identically distributed sample from this database, an automated method is employed. Since the winter storm dominates the extreme wave and winter storm samples are similar to independent non-tropical cyclone samples, especially for large samples, the extreme sample is extracted from the independent non-tropical cyclone sample. In most of the study region, large independent non-tropical cyclone samples occur evenly every year. The N-largest maxima (NLM) method is used for extraction, and the reasonable N values are determined by analysing the fitting result and extrapolation uncertainty and validated by the reasonable threshold range of the POT method. At a few locations, the large independent non-tropical cyclone sample rarely or never occurs in particular years. The diagnostic NLM method is proposed to exclude the small values. Benefitting from this method, a regional assessment of extreme SWHs is performed.

Species distribution modeling to inform transboundary species conservation and management under climate change: promise and pitfalls

Spatially explicit biogeographic models are among the most used methods in conservation biogeography, with correlative species distribution models (SDMs) being the most popular among them. SDMs can identify the potential for species’ and community range shifts under climate change, and thus can inspire, inform, and guide complex and adaptive conservation management planning efforts such as collaborative transboundary conservation frameworks. However, SDMs are rarely developed collaboratively, which would be ideal for conservation applications of such models. Further, SDMs that are applied to conservation often do not follow best practices of the field, which are particularly important for applications in climate change contexts for which model extrapolation into potentially novel climates is necessary. Thus, while there is substantial promise, particularly among machine-learning based SDM approaches, there are also many pitfalls to consider when applying SDMs to conservation, and especially in the context of transboundary management under climate change. Here, we summarize these pitfalls and the key steps to mitigate them and maximize the promise of applying SDMs to facilitate transboundary conservation planning under climate change. We argue that conservation modeling capacity must be elevated among practitioners such that they can easily implement best practices when using SDMs, especially regarding: 1) avoiding model overcomplexity, 2) addressing input data bias, and 3) accounting for uncertainty in model extrapolations and projections. While our discussion centers mainly on the pitfalls and opportunities of applying the most popular correlative SDM algorithm, Maxent, our suggestions can also be generalized to a range of other SDM tools. Overall, improved training in, tools for, and implementation of best practices in biogeographic models such as SDMs hold great promise to facilitate and help guide complex, transboundary collaborations for long-term planning of conservation under climate change.

Eliciting uncertainty for complex parameters in model-based economic evaluations: quantifying a temporal change in the treatment effect.

In model-based economic evaluations, the effectiveness parameter is often informed by studies with a limited duration of follow-up, requiring extrapolation of the treatment effect over a longer time horizon. Extrapolation from short-term data alone may not adequately capture uncertainty in that extrapolation. This study aimed to use structured expert elicitation to quantify uncertainty associated with extrapolation of the treatment effect observed in a clinical trial. A structured expert elicitation exercise was conducted for an applied study of a podiatry intervention designed to reduce the rate of falls and fractures in the elderly. A bespoke web application was used to elicit experts' beliefs about two outcomes (rate of falls and odds of fracture) as probability distributions (priors), for two treatment options (intervention and treatment as usual) at multiple time points. These priors were used to derive the temporal change in the treatment effect of the intervention, to extrapolate outcomes observed in a trial. The results were compared with extrapolation without experts' priors. The study recruited thirty-eight experts (geriatricians, general practitioners, physiotherapists, nurses, and academics) from England and Wales. The majority of experts (32/38) believed that the treatment effect would depreciate over time and expressed greater uncertainty than that extrapolated from a trial-based outcome alone. The between-expert variation in predicted outcomes was relatively small. This study suggests that uncertainty in extrapolation can be informed using structured expert elicitation methods. Using structured elicitation to attach values to complex parameters requires key assumptions and simplifications to be considered.

Numerical study of synthetic spherically expanding flames for optimization of laminar flame speed experiments

This study presents the performances of four commonly-used extrapolation models, including the linear stretch and linear curvature models, the nonlinear model in expansion form, and the nonlinear quasi-steady model, using synthetic data sets generated over a wide range of conditions. The effects of a number of experimental limitations, such as the facility size, the finite camera framing rate, and the noise in the image detected flame radius, were investigated. Two types of error, model and noise error, which constitute the overall extrapolation uncertainty were investigated. Relative model error is primarily driven by Markstein length (Lb) and the flame radius range; however, it is weakly sensitive to laminar flame speed (Sb0). Noise error is controlled by the size of flame radius data set which depends on framing rate and the selected flame radius range. For small values of |Lb|, the model error is negligible. For large values of |Lb|, the two error types are equally prevalent in the overall uncertainty and they cannot be simultaneously minimized. Experimentally, large experimental facilities and high camera framing rates should be favored to reduce the noise error. For extrapolation to zero-stretch, the model that best reproduces the flame propagation evolution should be favored. The Markstein length demonstrates a high sensitivity to the choice of model and noise addition when compared to the laminar flame speed. The procedures developed in this study can be used to predict extrapolation-induced error under various experimental conditions and can be used to optimize laminar flame speed experiments.

Creep Parameters Determination by Omega Model to Norton Bailey Law by Regression Analysis for Austenitic Steel SS-304

In the material’s creep failure analysis, the difficulty of assessing the applied thermo-mechanical boundary conditions makes it critically important. Numerous creep laws have been established over the years to predict the creep deformation, damage evolution and rupture of the materials subjected to creep phenomena. The omega model developed by the American Petroleum Institute and Material Properties Council is one of the most commonly used creep material models for numerical analysis over the years. It is good in defining the fitness of mechanical equipment for service engineering evaluation to ensure the reliable service life of the equipment. The Omega model, however, is not readily accessible and specifically incorporated for creep evaluation in FEA software codes and creep data is always scarce for the complete analysis. Therefore, extrapolation of creep behavior was performed by fitting various types of creep models with a limited amount of creep data and then simulating them, beyond the available data points. In conjunction with the Norton Bailey model, based on API-579/ASME FFS-1 standards, a curve fitting technique was employed called regression analysis. From the MPC project omega model, different creep strain rates were obtained based on material, stress and temperature-dependent data. In addition, as the strain rates increased exponentially with the increase in stresses, regression analysis was used for predicting creep parameters, that can curve fit the data into the embedded Norton Bailey model. The uncertainties in extrapolations and material constants has highlighted to necessitate conservative safety factors for design requirement. In this case study, FEA creep assessment was performed on the material SS-304 dog bone specimen, considered as a material coupon to predict time-dependent plastic deformation along with creep behavior at elevated temperatures and under constant stresses. The results indicated that the specimen underwent secondary creep deformation for most of the period.

Revisiting quark and gluon polarization in the proton at the EIC

We present a comprehensive impact study of future Electron-Ion Collider (EIC) data for parity-conserving and parity-violating polarization asymmetries on quark and gluon helicity distributions in the proton. The study, which is based on the JAM Monte Carlo global QCD analysis framework, explores the role of the extrapolation uncertainty and SU(3) flavor symmetry constraints in the simulated double-spin asymmetry, $A_{LL}$, at small parton momentum fractions $x$ and its effect on the extracted parton polarizations. We find that different assumptions about $A_{LL}$ extrapolations and SU(3) symmetry can have significant consequences for the integrated quark and gluon polarizations, for polarized proton, deuteron and $^3$He beams. For the parity-violating asymmetry, $A_{UL}$, we study the potential impact on the polarized strange quark distribution with different extrapolations of $A_{UL}$, finding the constraining power to be ultimately limited by the EIC machine luminosity.