Plausible Distributions Research Articles

Reducing the uncertainty in the distribution of cm-scale rock properties in the near well-bore region

Rock properties at cm-scale impact geological carbon storage by enhancing capillary and mineral trapping. Hence, it is important to accurately capture their distribution in geo-models which are used for numerically estimating the fate of injected CO2. However, there could be high variability in the cm-scale distribution of rock properties even close to wells which is not captured with traditional workflows. This study explores the impact of grid cell resolution, seismic inversion and placement of an additional well in proximity to CO2 injection well on improving the representation of cm-scale lithological heterogeneity in the near well bore region in geological models. We utilize wireline and seismic data from Parasequence-2 of the Paaratte Formation, Otway Basin, Australia, which is a shallow to coastal marine deltaic deposition comprising a high degree of lithological heterogeneity and a prospective unit for pilot scale geological carbon storage operations. The data was used to prepare a suite of reservoir models capturing the impact of above factors on the plausible distributions of facies, porosity and permeability in the formation. The analysis suggests that smaller grid cell size (1 m × 1 m × 0.3 m) compared to the typical industry standard (10 m × 10 m × 2 m) significantly improves the representation of cm-scale rock properties. Additionally, stochastic seismic inversion could play an important role in capturing rock property distribution even for smaller CO2 storage sites used for pilot scale injection operations. Further, we show that the placement of an additional well only 116-m away from the CO2 injection well can drastically improve the probability in the distribution of cm-scale rock properties in reservoir models.

RegionGrow3D: A Deterministic Analysis for Characterizing Discrete Three‐Dimensional Landslide Source Areas on a Regional Scale

AbstractRegional‐scale characterization of shallow landslide hazards is important for reducing their destructive impact on society. These hazards are commonly characterized by (a) their location and likelihood using susceptibility maps, (b) landslide size and frequency using geomorphic scaling laws, and (c) the magnitude of disturbance required to cause landslides using initiation thresholds. Typically, this is accomplished through the use of inventories documenting the locations and triggering conditions of previous landslides. In the absence of comprehensive landslide inventories, physics‐based slope stability models can be used to estimate landslide initiation potential and provide plausible distributions of landslide characteristics for a range of environmental and forcing conditions. However, these models are sometimes limited in their ability to capture key mechanisms tied to discrete three‐dimensional (3D) landslide mechanics while possessing the computational efficiency required for broad‐scale application. In this study, the RegionGrow3D (RG3D) model is developed to broadly simulate the area, volume, and location of landslides on a regional scale (≥1,000 km2) using 3D, limit‐equilibrium (LE)‐based slope stability modeling. Furthermore, RG3D is incorporated into a susceptibility framework that quantifies landsliding uncertainty using a distribution of soil shear strengths and their associated probabilities, back‐calculated from inventoried landslides using 3D LE‐based landslide forensics. This framework is used to evaluate the influence of uncertainty tied to shear strength, rainfall scenarios, and antecedent soil moisture on potential landsliding and rainfall thresholds over a large region of the Oregon Coast Range, USA.

A Robust Light-curve Diagnostic for Electron-capture Supernovae and Low-mass Fe-core-collapse Supernovae

Core-collapse supernovae (CCSNe) are the terminal explosions of massive stars. While most massive stars explode as iron-core-collapse supernovae (FeCCSNe), slightly less massive stars explode as electron-capture supernovae (ECSNe), shaping the low-mass end of CCSNe. ECSNe were proposed ∼40 yr ago and first-principles simulations also predict their successful explosions. Observational identification and investigation of ECSNe are important for the completion of stellar evolution theory. To date, only one promising candidate has been proposed, SN 2018zd, other than the historical progenitor of the Crab Nebula, SN 1054. We present representative synthetic light curves of low-mass FeCCSNe and ECSNe, i.e., with theoretically or observationally plausible explosion energies and distributions of circumstellar media (CSM). The ECSNe have shorter, brighter, and bluer plateaus than the FeCCSNe. To investigate the robustness of their intrinsic differences, we also calculated light curves of ECSNe and low-mass FeCCSNe adopting various explosion energies and CSM. Although they may have similar bolometric light-curve plateaus, ECSNe are bluer than FeCCSNe in the absence of strong CSM interaction, illustrating that multicolor observations are essential to identify ECSNe. This provides a robust indicator of ECSNe because the bluer plateaus stem from the low-density envelopes of their super-asymptotic-giant-branch progenitors. We propose a distance-independent method to identify ECSNe: g−rtPT/2<0.008×tPT−0.4 , i.e., blue g – r at the middle of the plateau (g−r)tPT/2 , where t PT is the transition epoch from plateau to tail. Using this method, we identified SN 2018zd as an ECSN, which we believe to be the first ECSN identified with modern observing techniques.

Refining tomography with generative neural networks trained from geodynamics

SUMMARY Inverse problems occur in many fields of geophysics, wherein surface observations are used to infer the internal structure of the Earth. Given the non-linearity and non-uniqueness inherent in these problems, a standard strategy is to incorporate a priori information regarding the unknown model. Sometimes a solution is obtained by imposing that the inverted model remains close to a reference model and with smooth lateral variations (e.g. a correlation length or a minimal wavelength are imposed). This approach forbids the presence of strong gradients or discontinuities in the recovered model. Admittedly, discontinuities, such as interfaces between layers, or shapes of geological provinces or of geological objects such as slabs can be a priori imposed or even suggested by the data themselves. This is however limited to a small set of possible constraints. For example, it would be very challenging and computationally expensive to perform a tomographic inversion where the subducting slabs would have possible top discontinuities with unknown shapes. The problem seems formidable because one cannot even imagine how to sample the prior space: is each specific slab continuous or broken into different portions having their own interfaces? No continuous set of parameters seems to describe all the possible interfaces that we could consider. To circumvent these questions, we propose to train a Generative Adversarial neural Network (GAN) to generate models from a geologically plausible prior distribution obtained from geodynamic simulations. In a Bayesian framework, a Markov chain Monte Carlo algorithm is used to sample the low-dimensional model space depicting the ensemble of potential geological models. This enables the integration of intricate a priori information, parametrized within a low-dimensional model space conducive to efficient sampling. The application of this approach is demonstrated in the context of a downscaling problem, where the objective is to infer small-scale geological structures from a smooth seismic tomographic image.

Relationship between intra-community noise tolerance distributions and fitted dose-response funtions

Increasingly, researchers are employing fitted exposure-response functions to not only characterize the relationship between sound level and prevalence of annoyance but also to compare the relative noise tolerance of individual communities. The sound level at which 50% of the population is annoyed has been advanced as a community noise tolerance metric. This paper combines prior research and recent simulations to reframe the discussion in terms of plausible distributions of individual (personal) noise tolerances within a given community; the integrals of those (density) distributions become the (cumulative) observed exposure-response relationships. Various tolerance distributions are developed, their resulting cumulative exposure-response relationships calculated, and the ability of commonly-employed functional forms to represent those cumulative relationships presented. The results of the current investigation strongly suggest that data sets which include annoyance fractions of 50% have the greatest chance of accurately predicting the 50%-annoyed sound level. The further the data set lies from the 50%-annoyed point the more dependent an extrapolated estimation becomes on the fitted functional form accurately representing the true cumulative relationship. Errors of estimation are shown for a number of tolerance distributions, functional forms and experimental data ranges. Finally, some remedial recommendations are offered.

An inter-comparison of approaches and frameworks to quantify irrigation from satellite data

Abstract. This study provides the first inter-comparison of different state-of-the-art approaches and frameworks that share a commonality in their utilization of satellite remote-sensing data to quantify irrigation at a regional scale. The compared approaches vary in their reliance on either soil moisture or evapotranspiration data or their joint utilization of both. The two compared frameworks either extract irrigation information from residuals between satellite observations and rainfed hydrological models in a baseline framework or use soil water balance modeling in a soil-moisture-based inversion framework. The inter-comparison is conducted over the lower Ebro catchment in Spain where observed irrigation amounts are available for benchmarking. Our results showed that within the baseline framework, the joint approach using both soil moisture and evapotranspiration (ET) remote-sensing data only differed by +37 mm from the irrigation benchmark (922 mm) during the main irrigation season over 2 years and by +47 and −208 mm for approaches relying solely on soil moisture and ET, respectively. A comparison of the different frameworks showed that the main advantage of the more complex baseline framework was the consistency between soil moisture and ET components within the hydrological model, which made it unlikely that either one ended up representing all irrigation water use. However, the simplicity of the soil-moisture-based inversion framework, coupled with its direct conversion of soil moisture changes into actual water volumes, effectively addresses the key challenges inherent in the baseline framework, which are associated with uncertainties related to an unknown remote-sensing observation depth and the static depth of the soil layers in a conceptual model. The performance of the baseline framework came closest to the irrigation benchmark and was able to account for the precipitation input, which resulted in more plausible temporal distributions of irrigation than what was expected from the benchmark observations.

Seismic Thermography

ABSTRACT Variations in temperature within the Earth are of great interest because they indicate the thickness and, consequently, mechanical strength of the lithosphere and density variations and convection patterns in the sublithospheric mantle. Seismic tomography maps seismic velocity variations in the mantle, which strongly depend on temperature. Temperatures are, thus, often inferred from tomography. Tomographic models, however, are nonunique solutions of inverse problems, regularized to ensure model smoothness or small model norm, not plausible temperature distributions. For example, lithospheric geotherms computed from seismic velocity models typically display unrealistic oscillations, with improbable temperature decreases with depth within shallow mantle lithosphere. The errors due to the intermediate-model nonuniqueness are avoided if seismic data are inverted directly for temperature. The recently developed thermodynamic inversion methods use computational petrology and thermodynamic databases to jointly invert seismic and other data for temperature and composition. Because seismic velocity sensitivity to composition is much weaker than to temperature, we can invert seismic data primarily for temperature, with reasonable assumptions on composition and other relevant properties and with additional inversion parameters such as anisotropy. Here, we illustrate thus-defined seismic thermography with thermal imaging of the lithosphere and asthenosphere using surface waves. We show that the accuracy of the models depends critically on the accuracy of the extraction of structural information from the seismic data. Random errors have little effect but correlated errors of even a small portion of 1% can affect the models strongly. We invert data with different noise characteristics and test a simple method to estimate phase velocity errors. Seismic thermography builds on the techniques of seismic tomography and relies on computational petrology, but it is emerging as a field with its scope of goals, technical challenges, and methods. It produces increasingly accurate models of the Earth, with important inferences on its dynamics and evolution.

Interseismic Coupling–Based Stochastic Slip Modeling of the 1920 Ms 8.5 Haiyuan Earthquake

Abstract The 1920 Ms 8.5 Haiyuan earthquake was the largest rupture in China in the twentieth century; however, the coseismic slip characteristics that provide insight into fault kinematics and future seismic hazards are unknown. In this study, we employed stochastic slip modeling to explore plausible slip distributions for this earthquake, incorporating different geodetic fault coupling models as prior constraints. Results demonstrate that fault coupling constraints have both positive and negative effects on stochastic models generating slip scenarios to fit the field-measured geomorphic offset observations. Notably, a Gauss coupling model helps to generate surface slip with higher probabilities to fit geomorphic offsets, exhibiting a closer resemblance to the slip distribution of the Haiyuan earthquake. Integrating 41 slip scenarios of 6000 that reasonably fit the geomorphic offsets, we find that the Haiyuan earthquake likely involved multiple asperities and occurred as a cascading rupture event. The western and eastern fault segments may involve shallow slip deficits, implying potential distributed and/or off-fault deformation during the earthquake, with implications for geomorphic offset–based interpretations of long-term fault behavior. The modeling not only provides insight into the kinematics of the Haiyuan earthquake but also offers a plausible scheme for investigating historical earthquakes.

Optical monitoring of the dust environment at lunar surface exploration sites

Lessons from the Apollo program showed that dust perturbed by human activities on the lunar surface can significantly interfere with the operation of mechanical, thermal and optical systems. During the Artemis program, monitoring the local dust environment created by surface activities will be critical to understanding and mitigating problems associated with lunar dust. This could be accomplished at many locations using in situ dust detectors; however, a complementary, and arguably more comprehensive, approach would be to measure the intensity of scattered sunlight from dust. Such measurements could be obtained using modest cameras and yield the abundance of dust along an observer line-of-sight. Observations along several look-directions would reveal the dust spatial distribution and constrain the minimum grain size from the angular width of the forward scattering lobe. Perhaps most importantly, these measurements would constrain spatial and temporal variations in dust ejection and deposition rates. Using scattering properties for realistically shaped lunar dust grains, this study simulates spectral intensities for a plausible steady-state distribution of low-speed (∼ m s−1) dust around an exploration site. This is used to assess the feasibility of using commonly available wide-angle optics and commercial off-the-shelf (COTS) image sensors to create a notional dust monitoring camera. The dust detection sensitivity of cameras constructed using components with flight heritage is modeled; e.g., the PL1 image sensor aboard the Light Italian CubeSat for Imaging of Asteroids (LICIACube) part of NASA's Double Asteroid Redirect Test (DART) mission. Results indicate that a simple but well-baffled camera can successfully detect a steady-state dust cloud created by human (artificial) activity with a peak concentration of < 1 μg m−3 for a polar site on the Moon—well below what might be expected during Artemis operations.

Cost-effectiveness analysis of atezolizumab as adjuvant treatment of patients with stage II-IIIA non-small cell lung cancer, PD-L1+≥50% of tumor cells in France: A modeling study

IntroductionThe objective of this study was to assess the cost-effectiveness of atezolizumab versus best supportive care (BSC) as adjuvant treatment following resection and platinum-based chemotherapy for patients with stage II-IIIA non-small cell lung cancer (NSCLC) whose tumours have a programmed death-ligand 1 (PD-L1) expression ≥ 50% of tumour cells and excluding those with ALK/EGFR mutations, from a French collective perspective. Material and MethodsA five state Markov model over a 20-year time horizon was considered, including disease-free survival (DFS11Abbreviations: AE: adverse event; ALK: anaplastic lymphoma kinase; BSC: best supportive care; DFS: disease-free survival; DSA: deterministic sensitivity analyses; EFGR: epidermal growth factor receptor; EQ-5D: EuroQol-5 dimension; HAS: Haute Autorité de Santé; ICs: immune cells; ICER: incremental cost-effectiveness ratio; IgG1: immunoglobulin G1; IV: intravenous; KM: Kaplan-Meier; LY: life-year; NSCLC: non-small cell lung cancer; OS: overall survival; PD-L1: programmed death-ligand 1; PSA: probabilistic sensitivity analysis; QALY: quality-adjusted life-year; QoL: quality of life; TCs: tumor cells.) from IMpower010 trial, three progression states (locoregional recurrence, first and second-line metastatic recurrence) and death. Utilities, quality-adjusted life year (QALY) decrements associated to adverse events, costs, resource use, and transition probabilities were considered in the model. These inputs were sourced from IMpower010 trial, literature, and clinical experts’ opinion. Model uncertainty was assessed through deterministic, probabilistic sensitivity analyses and scenario analyses. ResultsAtezolizumab was associated with a QALY gain of 1.662, mainly driven by additional time spent in the DFS state, and a life-year gain of 2.112 years. The incremental cost-effectiveness ratio (ICER) for atezolizumab versus BSC was €21,348/QALY gained. The sensitivity analyses highlighted that uncertainty within the model had limited impact on results. Changing the DFS survival curves to other plausible distributions produced ICERs below €20,000/QALY. Introducing an increasing proportion of cured patients (91.5%) from year two to year five reduced the ICER to €13,083/QALY, while including a loss of efficacy at year two in the atezolizumab treatment arm increased the ICER to €33,755/QALY. DiscussionAtezolizumab as adjuvant treatment in stage II-IIIA NSCLC resected patients with PDL1 ≥ 50% and without ALK/EGFR mutations has a lower ICER than other oncology drugs in France and a similar ICER to other adjuvant treatment in oncology.

Laboratory and field evaluation of a low-cost optical particle sizer

Low-cost sensors are widely used to collect high-spatial-resolution particulate matter data that traditional reference monitoring devices cannot. In addition to the mass concentration, the number concentration and size distribution are also fundamental in determining the origin and hazard level of particulate pollution. Therefore, low-cost optical sensors have been improved to establish optical particle sizers (OPSs). In this study, a low-cost OPS, the Nova SDS029, is introduced, and it is evaluated in comparison to two reference instruments—the GRIMM 11-D and the TSI 3330. We first tested the sizing accuracy using polystyrene latex spheres. Then, we assessed the mass and number size distribution accuracy in three application scenarios: indoor smoking, ambient air quality, and mobile monitoring. The evaluations suggest that the low-cost SDS029 rivals research-grade optical sizers in many aspects. For example, (1) the particle diameters obtained with the SDS029 are close to the reference instruments (usually < 10%) in the 0.3–5 µm range; (2) the number of particles and mass concentration are highly correlated (r ≥ 0.99) with the values obtained with the reference instruments; and (3) the SDS029 slightly underestimates the number concentration, but the derived PM2.5 values are closer to monitoring station than the reference instruments. The successful application of the SDS029 in multiple scenarios suggests that a plausible particle size distribution can be obtained in an easy and cost-efficient way. We believe that low-cost OPSs will increasingly be used to map the sources and risk levels of particles at the city scale.

Semantic-gap-oriented feature selection in hierarchical classification learning

Semantic gap is common issues in classification tasks. Different from traditional classification tasks, the semantic gap problem can introduce serious error propagation for hierarchical classification. In this paper, we propose a hierarchical feature selection method with label enhancement (HFSLE) to solve this challenge, which estimates the label distribution and selects subsets of features for each subtask. Firstly, HFSLE combines the label enhancement and hierarchical feature selection process so that the label enhancement benefits from the feedback of the loss function. Then, the label enhancement is constrained by the consistency of the parent-child distribution to obtain a plausible label distribution. Finally, feature selection is optimized based on intra-class consistency hierarchical regularization terms, which unifies feature subsets with hierarchical semantic descriptions to narrow the semantic gap. HFSLE gives a new perspective on the hierarchical feature selection based on label distribution rather than the original logical label. To demonstrate the superiority of the method, we compared it with six well-established feature selection methods on eight hierarchical data sets, and analyzed the convergence and robustness of the method. Extensive experiments demonstrate that the proposed method can perform favorably against advanced hierarchical feature selection approaches.

Веса косвенных измерений в обратных задачах фотограмметрии

This is the fourth article in the series of publications on ways of improving the statistical quality of solving inverse photogrammetry tasks. It deals with researching the impact of indirect measurement weights on the equalization’s statistical quality. The author established that following its criteria is the key to the most plausible distribution of errors on the effects of indirect measurements. Since the accuracy of the equalization is estimated by the remainder residuals of the indirect measurements groups, the reliability of these evaluations depends on how strictly the criteria for the statistical quality of the equalization are met. The standard of indicators’ accuracy reliability is obtained. An algorithm for adjusting their weights during equalization was developed. An experimental approbation of the technique was performed. Based on the results of the calculations, the effectiveness of the joint application of all ways developed in this and previous articles is established to improve the statistical quality and obtain a reliable estimate of the equalization computations accuracy. In addition, it turned out that the application of the developed algorithms enables increasing the numerical stability of the mentioned calculations and ensure the stability of the results. The expediency of clarifying the initial values of direct measurements of the equalized parameters is confirmed. The explanation of the obtained results in terms of covariance analysis is given.

Utilization of convolutional neural networks for H I source finding

Context. The future deployment of the Square Kilometer Array (SKA) will lead to a massive influx of astronomical data and the automatic detection and characterization of sources will therefore prove crucial in utilizing its full potential. Aims. We examine how existing astronomical knowledge and tools can be utilized in a machine learning-based pipeline to find 3D spectral line sources. Methods. We present a source-finding pipeline designed to detect 21-cm emission from galaxies that provides the second-best submission of SKA Science Data Challenge 2. The first pipeline step was galaxy segmentation, which consisted of a convolutional neural network (CNN) that took an H I cube as input and output a binary mask to separate galaxy and background voxels. The CNN was trained to output a target mask algorithmically constructed from the underlying source catalog of the simulation. For each source in the catalog, its listed properties were used to mask the voxels in its neighborhood that capture plausible signal distributions of the galaxy. To make the training more efficient, regions containing galaxies were oversampled compared to the background regions. In the subsequent source characterization step, the final source catalog was generated by the merging and dilation modules of the existing source-finding software SOFIA, and some complementary calculations, with the CNN-generated mask as input. To cope with the large size of H I cubes while also allowing for deployment on various computational resources, the pipeline was implemented with flexible and configurable memory usage. Results. We show that once the segmentation CNN has been trained, the performance can be fine-tuned by adjusting the parameters involved in producing the catalog from the mask. Using different sets of parameter values offers a trade-off between completeness and reliability.

Normative models for neuroimaging markers: Impact of model selection, sample size and evaluation criteria

Modelling population reference curves or normative modelling is increasingly used with the advent of large neuroimaging studies. In this paper we assess the performance of fitting methods from the perspective of clinical applications and investigate the influence of the sample size. Further, we evaluate linear and non-linear models for percentile curve estimation and highlight how the bias-variance trade-off manifests in typical neuroimaging data.We created plausible ground truth distributions of hippocampal volumes in the age range of 45 to 80 years, as an example application. Based on these distributions we repeatedly simulated samples for sizes between 50 and 50,000 data points, and for each simulated sample we fitted a range of normative models. We compared the fitted models and their variability across repetitions to the ground truth, with specific focus on the outer percentiles (1st, 5th, 10th) as these are the most clinically relevant.Our results quantify the expected decreasing trend in variance of the volume estimates with increasing sample size. However, bias in the volume estimates only decreases a modest amount, without much improvement at large sample sizes. The uncertainty of model performance is substantial for what would often be considered large samples in a neuroimaging context and rises dramatically at the ends of the age range, where fewer data points exist. Flexible models perform better across sample sizes, especially for non-linear ground truth.Surprisingly large samples of several thousand data points are needed to accurately capture outlying percentiles across the age range for applications in research and clinical settings. Performance evaluation methods should assess both bias and variance. Furthermore, caution is needed when attempting to go near the ends of the age range captured by the source data set and, as is a well known general principle, extrapolation beyond the age range should always be avoided. To help with such evaluations of normative models we have made our code available to guide researchers developing or utilising normative models.

The impact of the parameters of the constitutive model on the distribution of strain in the femoral head

The rapid spread of the finite element method has caused that it has become, among other methods, the standard tool for pre-clinical estimates of bone properties. This paper presents an application of this method for the calculation and prediction of strain and stress fields in the femoral head. The aim of the work is to study the influence of the considered anisotropy and heterogeneity of the modeled bone on the mechanical fields during a typical gait cycle. Three material models were tested with different properties of porous bone carried out in literature: a homogeneous isotropic model, a heterogeneous isotropic model, and a heterogeneous anisotropic model. In three cases studied, the elastic properties of the bone were determined basing on the Zysset-Curnier approach. The tensor of elastic constants defining the local properties of porous bone is correlated with a local porosity and a second order fabric tensor describing the bone microstructure. In the calculations, a model of the femoral head generated from high-resolution tomographic scans was used. Experimental data were drawn from publicly available database “Osteoporotic Virtual Physiological Human Project.” To realistically reflect the load on the femoral head, main muscles were considered, and their contraction forces were determined based on inverse kinematics. For this purpose, the results from OpenSim packet were used. The simulations demonstrated that differences between the results predicted by these material models are significant. Only the anisotropic model allowed for the plausible distribution of stresses along the main trabecular groups. The outcomes also showed that the precise evaluation of the mechanical fields is critical in the context of bone tissue remodeling under mechanical stimulations.

Dust Rings and Cavities in the Protoplanetary Disks around HD 163296 and DoAr 44

We model substructure in the protoplanetary disks around DoAr 44 and HD 163296 in order to better understand the conditions under which planets may form. We match archival millimeter-wavelength thermal emission against models of the disks’ structure that are in radiation balance with the starlight heating and in vertical hydrostatic equilibrium, and then we compare to archival polarized scattered near-infrared images of the disks. The millimeter emission arises in the interior, while the scattered near-infrared radiation probes the disks’ outer layers. Our best model of the HD 163296 disk has dust masses 81 ± 13 M ⊕ in the inner ring at 68 au and M ⊕ in the outer ring at 102 au, both falling within the range of estimates from previous studies. Our DoAr 44 model has total dust mass M ⊕. Unlike HD 163296, DoAr 44 as of yet has no detected planets. If the central cavity in the DoAr 44 disk is caused by a planet, the planet’s mass must be at least 0.5 M J and is unlikely to be greater than 1.6 M J. We demonstrate that the DoAr 44 disk's structure with a bright ring offset within a fainter skirt can be formed by dust particles drifting through a plausible distribution of gas.

Using Prior Toxicological Data to Support Dose-Response Assessment─Identifying Plausible Prior Distributions for Dichotomous Dose-Response Models.

The benchmark dose (BMD) methodology has significantly advanced the practice of dose-response analysis and created substantial opportunities to enhance the plausibility of BMD estimation by synthesizing dose-response information from different sources. Particularly, integrating existing toxicological information via prior distribution in a Bayesian framework is a promising but not well-studied strategy. The study objective is to identify a plausible way to incorporate toxicological information through informative prior to support BMD estimation using dichotomous data. There are four steps in this study: determine appropriate types of distribution for parameters in common dose-response models, estimate the parameters of the determined distributions, investigate the impact of alternative strategies of prior implementation, and derive endpoint-specific priors to examine how prior-eliciting data affect priors and BMD estimates. A plausible distribution was estimated for each parameter in the common dichotomous dose-response models using a general database. Alternative strategies for implementing informative prior have a limited impact on BMD estimation, but using informative prior can significantly reduce uncertainty in BMD estimation. Endpoint-specific informative priors are substantially different from the general one, highlighting the necessity for guidance on prior elicitation. The study developed a practical way to employ informative prior and laid a foundation for advanced Bayesian BMD modeling.

The Estimation of the Total Freeboard of Arctic Sea Ice in Winter Using Passive Microwave Satellite Measurements

Abstract A method for estimating the total freeboard hf of the sea ice in the Arctic basin was developed in this study. To utilize the dielectric properties of microwave measurements on the sea ice freeboard, we adopted the spectral difference between the microwave frequencies (e.g., the gradient ratio). Satellite lidar altimetry data were utilized as a reference, and two pairs of gradient ratios [GR(36.5, 18.7) and GR(10.7, 6.9)] and an optional brightness temperature for first-year ice [that is, TBH(6.9)] were converted from passive microwave sensors into hf using the multiple linear regression equation. Using this method, we estimated hf without direct altimetry measurements. The developed method was evaluated using Operation IceBridge data and the relationship between the regressed hf and Operation IceBridge hf had a correlation coefficient R of 0.761 and a nearly unbiased (approximately −0.4 cm) pattern. Because passive microwave measurements are taken in the Arctic daily, the approach presented in this study has the potential to enable daily hf estimations for the Arctic. Significance Statement Arctic sea ice is one of the most critical indicators when monitoring climate change. Precise and continuous observations of sea ice thickness are essential to understand Arctic sea ice. This study attempts to estimate sea ice thickness using passive microwave satellite data. Passive microwave satellite observations are advantageous because of their wide spatial coverage and long-term records. Therefore, the suggested method in this study can be used for filling in gaps in coverage between sea ice thickness estimates from L-band radiometry and radar/lidar altimetry. The total freeboard is proportional to the thickness of sea ice, which is converted into thickness using the hydrostatic equation. The estimated total freeboard during two winter periods (2018/19 and 2019/20) demonstrates a plausible geographical distribution over the Arctic and indicates good agreement with airborne measurements.

Estimation of Low Flow Statistics for Sustainable Water Resources Management in South Australia

The Magnitude and occurrence of extreme low flow events are needed in setting minimum flows to protect the instream users. As the true distribution is not normally known, the identification of the most appropriate distribution function that describes the extreme low flow data of a catchment is essential in estimating reliable low flow quantiles at various average recurrence intervals (ARI). The aim of this study is to conduct a comparative assessment of the performance of three plausible distribution functions for estimating low flow quantiles. The investigation was carried out by using 27-gauge stations within South Australia (SA), the driest state in Australia. The best distribution function out of the three selected distributions; Log Normal (LN), Log Pearson Type 3 (LP3), and Generalized Extreme Value (GEV for each of the three selected annual minima series (7-day, 15-day and 30-day) at each gauged catchments was identified. The estimated low flow quantiles from using these three distribution functions were compared using RMSE values estimated through Monte Carlo simulation studies. For the majority of the selected study catchments, GEV fitted using L moments was found to be the best method for estimating low flow quantiles at ARIs over 10 years (≥14%), while at low ARI, LP3 fitted using the Method of Moments (MOM) was shown to outperform (≥17%) the other methods.