Account Model Uncertainty Research Articles

In-silico mechanistic analysis of adsorption of Iodinated Contrast Media agents on graphene surface

The study aims at assessing the potential of graphene-based adsorbents to reduce environmental impacts of Iodinated Contrast Media Agents (ICMs). We analyze an extensive collection of ICMs. A modeling approach resting on molecular docking and Density Functional Theory simulations is employed to examine the adsorption process at the molecular level. The study also relies on a Quantitative Structure-Activity Relationship (QSAR) modeling framework to correlate molecular properties with the adsorption energy (Ead) of ICMs, thus enabling identification of the key mechanisms underpinning adsorption and of the key factors contributing to it. A collection of distinct QSAR-based models is developed upon relying on Multiple Linear Regression and a standard genetic algorithm method. Having at our disposal multiple models enables us to take into account the uncertainty associated with model formulation. Maximum Likelihood and formal model identification/discrimination criteria (such as Bayesian and/or information theoretic criteria) are then employed to complement the traditional QSAR modeling phase. This has the advantage of (a) providing a rigorous ranking of the alternative models included in the selected set and (b) quantifying the relative degree of likelihood of each of these models through a weight or posterior probability. The resulting workflow of analysis enables one to seamlessly embed DFT and QSAR studies within a theoretical framework of analysis that explicitly takes into account model and parameter uncertainty. Our results suggest that graphene-based surfaces constitute a promising adsorbent for ICMs removal, π-π stacking being the primary mechanism behind ICM adsorption. Furthermore, our findings offer valuable insights into the potential of graphene-based adsorbent materials for effectively removing ICMs from water systems. They contribute to ascertain the significance of various factors (such as, e.g., the distribution of atomic van der Waals volumes, overall molecular complexity, the presence and arrangement of Iodine atoms, and the presence of polar functional groups) on the adsorption process.

Research on adaptive feedforward control method for Tiltrotor Aircraft/Turboshaft engine system based on radial basis function neural network

The paper proposes an adaptive feedforward control approach based on a radial basis function neural network to address the issue of poor disturbance rejection capability in conventional integrated control methods for tiltrotor aircraft propulsion systems. Firstly, a comprehensive simulation model of the tiltrotor aircraft and turboshaft engine is established, and its accuracy is validated against the results of the General Tiltrotor Integrated Simulation Code. Building upon this, a cascade Proportional-integral control parameter design method based on separation principles is proposed to tackle the problem of designing control parameters for the Proportional Integral main loop structure. Subsequently, an adaptive feedforward control method that takes into account both aircraft-induced disturbances and model uncertainties for turboshaft engines is presented using a Radial Basis Function neural network. This approach includes a nonlinear estimator capable of estimating load disturbances and a Radial Basis Function adaptive feedforward controller. Furthermore, stability proof of the closed-loop system is provided based on Lyapunov function analysis. Finally, digital simulations and hardware-in-the-loop experiments are conducted based on a typical tiltrotor flight mission. The results demonstrate that the proposed method effectively mitigates the power turbine drop compared to the conventional Proportional Integral controller with collective pitch feedforward.

Fluorine abundance of the lunar magma ocean constrained by experimentally determined mineral-melt F partitioning

To quantify fluorine (F) evolution during lunar magma ocean (LMO) crystallization, high-pressure, high-temperature experiments have been conducted to determine mineral-melt partitioning of F for lunar minerals (plagioclase, orthopyroxene and ilmenite). Results constrain the F abundance in the magma ocean to 21–41 ppm at the time crust-forming plagioclase started crystallizing. Forward modeling shows that 352–703 ppm F would remain in the final 1 % of magma toward the end of magma ocean solidification. This range overlaps that inferred for the urKREEP reservoir (660 ppm). Taking into account model uncertainties, from the perspective of F abundances the urKREEP reservoir can be formed at 98.9–99.5% LMO solidification, with negligible loss of F from the Moon since the onset of crust formation. Backward modeling from initial crust-forming plagioclase, an initial LMO would contain 4.2–8.5 ppm F, which is consistent with estimates of the lunar primitive mantle F content derived from melt inclusions in Apollo samples. This finding is consistent with previous suggestions that the bulk silicate Moon is depleted in F relative to the bulk silicate Earth (which contains ∼25 ppm F). A BSE-like initial LMO would yield a magma containing 122 ppm F at the onset of crust formation, significantly higher than our calculated 21–41 ppm F. Fluorine depletion could have occurred by degassing during the early LMO stages (between the onset of LMO crystallization and first crust formation), and/or prior to the LMO stage (e.g., depletion during the giant impact or vapor drainage in the protolunar disk), but seems to have ended by the time the crust started forming.

Adaptive Fuzzy Event-Triggered Cooperative Control for Multi-Robot Systems: A Predefined-Time Strategy.

A predefined-time adaptive fuzzy cooperative controller with event triggering is proposed for multi-robot systems that takes into account external disturbances, input saturation, and model uncertainties in this paper. First, based on the asymmetric tan-type barrier Lyapunov function, a predefined-time controller is proposed to acquire a quick response and more precise convergence time under the directed communication topology. Second, predefined-time fuzzy logic systems are developed to approximate external disturbances and model uncertainties. Third, a dynamic relative threshold event-triggered mechanism is improved to save the communication resources of the robots. Subsequently, the proof procedure for the predefined-time stability is given using the Lyapunov stability theorem. Finally, some simulation examples, including a comparative experi-ment on multi-robot systems, are provided to test the effectiveness of the above algorithm.

Consensus-Based Distributed Cooperative Perception for Connected and Automated Vehicles

Cooperative perception techniques incorporating Vehicle-to-Everything (V2X) information offer new possibilities for enhancing the perception capability of automated vehicles (AVs), but also present a new challenge of how to maximize the benefits of connected information with limited communication burden. In this context, this paper proposes a novel cooperative perception solution based on consensus theory to improve the accuracy and consistency for the detection and tracking of non-connected targets by combining V2X information. Given the common multi-sensor configurations of AVs, we design a consensus-based distributed cooperative perception (DCP) algorithm in the framework of multi-layer information fusion for local sensors and connected nodes, and give a nonlinear form based on cubature rules to include more accurate nonlinear system models and nonlinear sensors. Considering the high maneuverability of vehicle targets, we then extend the DCP algorithm to a multi-model form (DMMCP) to improve the model uncertainty of maneuvering targets via combining the prior knowledge of multiple models, which also gives a calculation method for model probability and its average consensus in the context of multiple local sensors. Besides, a new consensus information weight strategy and the properties from different consensus information weights are discussed. The simulation results demonstrate the superiority of both our algorithms over the traditional algorithms in accuracy and consistency, moreover, the DMMCP algorithm, which takes into account model uncertainty, shows better performance than DCP in complex conditions.

Growth and inflation tradeoffs of dollarization: Meta-analysis evidence

We examine 585 estimates for macroeconomic effects of dollarization reported in 43 studies, codify 39 aspects of study design that may influence the estimates, and use Bayesian model averaging to take into account model uncertainty in meta-analysis. The results indicate that dollarized countries on average display slower and more volatile output growth, and a lower inflation rate than non-dollarized countries, but the estimates vary widely both within and across studies. Fully dollarized countries on average exhibit slower growth than non-dollarized and partially dollarized economies. Dollarization measurement, empirical method selection and authors’ affiliation systematically affect reported dollarization effects. The empirical results also suggest that limited control over foreign currency’s money supply in dollarized economies reduces the central banks’ ability to effectively conduct monetary policy and adjust to macroeconomic shocks.

NURBS-based surface generation from 3D images: spectral construction and data-driven model selection

Abstract In this paper, we present a set of improved algorithms for recovering computer aided design (CAD-type) surface models from three-dimensional (3D) images. The goal of the proposed framework is to generate B-spline or non-uniform rational B-spline (NURBS) surfaces, which are standard mathematical representations of solid objects in digital engineering. To create a NURBS surface, we first compute a control network (a quadrilateral mesh) from a triangular mesh using the Marching Cubes algorithm and Discrete Morse theory. To create a NURBS surface, we first compute a triangular mesh using the Marching Cubes algorithm, then the control network (a quadrilateral mesh) is determined from the triangular mesh by using Discrete Morse theory. Discrete Morse theory uses the critical points of a specific scalar field defined over the triangulation to generate a quad mesh. Such a scalar field is obtained by solving a graph Laplacian eigenproblem over the triangulation. However, the resulting surface is not optimal. We therefore introduce an optimization algorithm to better approximate the geometry of the object. In addition, we propose a statistical method for selecting the most appropriate eigenfunction of the graph Laplacian to generate a control network that is neither too coarse nor too fine, given the precision of the 3D image. To do this, we set up a regression model and use an information criterion to choose the best surface. Finally, we extend our approach by taking into account both model and data uncertainty using probabilistic regression and sampling the posterior distribution with Hamiltonian Markov Chain Monte Carlo.

Comparison of Deterministic and Probabilistic Variational Data Assimilation Methods Using Snow and Streamflow Data Coupled in HBV Model for Upper Euphrates Basin

The operation of upstream reservoirs in mountainous regions fed by snowmelt is highly challenging. This is partly due to scarce information given harsh topographic conditions and a lack of monitoring stations. In this sense, snow observations from remote sensing provide additional and relevant information about the current conditions of the basin. This information can be used to improve the model states of a forecast using data assimilation techniques, therefore enhancing the operation of reservoirs. Typical data assimilation techniques can effectively reduce the uncertainty of forecast initialization by merging simulations and observations. However, they do not take into account model, structural, or parametric uncertainty. The uncertainty intrinsic to the model simulations introduces complexity to the forecast and restricts the daily work of operators. The novel Multi-Parametric Variational Data Assimilation (MP-VarDA) uses different parameter sets to create a pool of models that quantify the uncertainty arising from model parametrization. This study focuses on the sensitivity of the parametric reduction techniques of MP-VarDA coupled in the HBV hydrological model to create model pools and the impact of the number of parameter sets on the performance of streamflow and Snow Cover Area (SCA) forecasts. The model pool is created using Monte Carlo simulation, combined with an Aggregated Distance (AD) Method, to create different model pool instances. The tests are conducted in the Karasu Basin, located at the uppermost part of the Euphrates River in Türkiye, where snowmelt is a significant portion of the yearly runoff. The analyses were conducted for different thresholds based on the observation exceedance probabilities. According to the results in comparison with deterministic VarDA, probabilistic MP-VarDA improves the m-CRPS gains of the streamflow forecasts from 57% to 67% and BSS forecast skill gains from 52% to 68% when streamflow and SCA are assimilated. This improvement rapidly increases for the first additional model parameter sets but reaches a maximum benefit after 5 parameter sets in the model pool. The improvement is notable for both methods in SCA forecasts, but the best m-CRPS gain is obtained for VarDA (31%), while the best forecast skill is detected in MP-VarDA (12%).

Robust determinants of the shadow economy

AbstractThis study examines the determinants of the shadow economy by employing Bayesian Model Averaging technique, which allows taking into account model uncertainty. Having estimated millions of combinations of models, the study revealed that while higher GDP growth rate, trade openness, and better institutional quality lead to the reduction of the informal sector, higher rate of unemployment, complicated regulations, and the large size of government are associated with a greater size of the shadow economy. Lessening bureaucratic complexity by eliminating burdensome regulations could help to reduce both wasteful spending of government on operating these regulations and the size of the underground economy.

A novel method for optimal robust PID design based on mixed sensitivity closed loop shaping and convex programming

In industry, the PID controller remains ubiquitous. Its design is typically tackled from classical design methods which do not properly take into account model uncertainty. Robustness is therefore typically a major issue with classically tuned PID controllers. This in turn results in PID controllers that are ‘detuned’ experimentally by an operator in order to avoid these robustness issues. A more generic method is hence required for tuning robust PID controllers.This paper proposes a novel method to enforce a PID (classic and higher order PID) architecture in the mixed sensitivity controller synthesis problem (which is the basis for the mixed sensitivity H∞ design), based on sequential convex second order programming with guaranteed convergence to a stationary point of the optimization problem.Numerical validation of the proposed method is presented by means of some toy problems illustrating the flexibility and practicality of the proposed method. The resulting PID controllers are compared with classical H∞ controllers illustrating the viability of the method to synthesise robust PID controllers based on H∞ controller design specification.

CMIP6 다중모델 기반 동아시아 현재기후 재현성 평가

This study evaluates the performances of 38 global climate models used in the Coupled Model Intercomparison Project Phase 6 (CMIP6) for simulating present-day (1995 ~ 2014) climatological temperature and precipitation for the East Asian region. The simulated results of the CMIP6 multi-model ensembles show that overall spatial distribution is similar to that of the reanalysis data. In winter (December-January-February; DJF), temperature and precipitation biases are -0.62°C and 0.11 mm, respectively. Also, in summer (June-July-August; JJA), temperature and precipitation biases are -0.28°C and 0.22 mm, respectively. Additionally, we evaluate four climate extreme indices (TXx, TNn, R95p, Rx5day) compared with ERA5 and HadEX3 data. Simulated extreme temperature-related indices are well reproduced in the present-day, although the daily maximum and minimum temperature show negative and positive bias, respectively. Using the precipitation indies, the result of CMIP6 models is underestimated compared to reanalysis data. This is a typical feature of the East Asian Summer Monsoon simulated from CMIP climate models. Taking into account model validation and uncertainty estimation, this study demonstrates simulated performance of CMIP6 models for the present-day period.

Uncertainty Quantification of Regional Cardiac Tissue Properties in Arrhythmogenic Cardiomyopathy Using Adaptive Multiple Importance Sampling.

Introduction: Computational models of the cardiovascular system are widely used to simulate cardiac (dys)function. Personalization of such models for patient-specific simulation of cardiac function remains challenging. Measurement uncertainty affects accuracy of parameter estimations. In this study, we present a methodology for patient-specific estimation and uncertainty quantification of parameters in the closed-loop CircAdapt model of the human heart and circulation using echocardiographic deformation imaging. Based on patient-specific estimated parameters we aim to reveal the mechanical substrate underlying deformation abnormalities in patients with arrhythmogenic cardiomyopathy (AC).Methods: We used adaptive multiple importance sampling to estimate the posterior distribution of regional myocardial tissue properties. This methodology is implemented in the CircAdapt cardiovascular modeling platform and applied to estimate active and passive tissue properties underlying regional deformation patterns, left ventricular volumes, and right ventricular diameter. First, we tested the accuracy of this method and its inter- and intraobserver variability using nine datasets obtained in AC patients. Second, we tested the trueness of the estimation using nine in silico generated virtual patient datasets representative for various stages of AC. Finally, we applied this method to two longitudinal series of echocardiograms of two pathogenic mutation carriers without established myocardial disease at baseline.Results: Tissue characteristics of virtual patients were accurately estimated with a highest density interval containing the true parameter value of 9% (95% CI [0–79]). Variances of estimated posterior distributions in patient data and virtual data were comparable, supporting the reliability of the patient estimations. Estimations were highly reproducible with an overlap in posterior distributions of 89.9% (95% CI [60.1–95.9]). Clinically measured deformation, ejection fraction, and end-diastolic volume were accurately simulated. In presence of worsening of deformation over time, estimated tissue properties also revealed functional deterioration.Conclusion: This method facilitates patient-specific simulation-based estimation of regional ventricular tissue properties from non-invasive imaging data, taking into account both measurement and model uncertainties. Two proof-of-principle case studies suggested that this cardiac digital twin technology enables quantitative monitoring of AC disease progression in early stages of disease.

Combining integral transform and a generalized probabilistic approach of uncertainties to quantify model-parameter and model uncertainties in computational structural dynamics: The stochastic GITT method

The current work combines the generalized probabilistic approach of uncertainties recently developed by Soize (2010)—with the generalized integral transform technique in order to take into account two types of uncertainties: (i) model-parameter uncertainties and (ii) model uncertainties induced by modelling errors. The generalized integral transform technique (or GITT) is a powerful meshless hybrid analytical-numerical approach based on eigenfunction expansions to solve systems of partial differential equations and has been progressively advanced during the last three decades. The current work advances the state-of-the-art of the GITT approach by rigorously taking into account both model-parameter uncertainties and model uncertainties to improve the predictive accuracy of computational models developed in the context of computational structural dynamics. The developed stochastic GITT method is flexible enough to handle parametric and non-parametric probabilistic methods to quantify both types of uncertainties. It is applied to the governing equations derived with the extended Hamilton’s variational principle for predicting the in-plane and out-of-plane bending vibrations of a slender flexible structure connected to a rotating rigid shaft, resembling many complex engineered structures. Stochastic dynamic analyses in both time- and frequency-domains under both types of uncertainties are firstly carried out. Finally, stochastic model updating is carried out with the maximum likelihood and Bayesian statistical methods in order to construct both the optimum prior stochastic models of uncertainties and the posterior stochastic model of model-parameter uncertainties, using the first natural frequencies as observed data.

A Primer on Bayesian Model-Averaged Meta-Analysis

Meta-analysis is the predominant approach for quantitatively synthesizing a set of studies. If the studies themselves are of high quality, meta-analysis can provide valuable insights into the current scientific state of knowledge about a particular phenomenon. In psychological science, the most common approach is to conduct frequentist meta-analysis. In this primer, we discuss an alternative method, Bayesian model-averaged meta-analysis. This procedure combines the results of four Bayesian meta-analysis models: (a) fixed-effect null hypothesis, (b) fixed-effect alternative hypothesis, (c) random-effects null hypothesis, and (d) random-effects alternative hypothesis. These models are combined according to their plausibilities given the observed data to address the two key questions “Is the overall effect nonzero?” and “Is there between-study variability in effect size?” Bayesian model-averaged meta-analysis therefore avoids the need to select either a fixed-effect or random-effects model and instead takes into account model uncertainty in a principled manner.

Optimization of Aerosol Model Selection for TROPOMI/S5P

To retrieve aerosol properties from satellite measurements, micro-physical aerosol models have to be assumed. Due to the spatial and temporal inhomogeneity of aerosols, choosing an appropriate aerosol model is an important task. In this paper, we use a Bayesian algorithm that takes into account model uncertainties to retrieve the aerosol optical depth and layer height from synthetic and real TROPOMI O2A band measurements. The results show that in case of insufficient information for an appropriate micro-physical model selection, the Bayesian algorithm improves the accuracy of the solution.

Human Multi-Robot Physical Interaction: a Distributed Framework

The objective of this paper is to devise a general framework to allow a human operator to physically interact with an object manipulated by a multi-manipulator system in a distributed setting. A two layer solution is devised. In detail, at the top layer an arbitrary virtual dynamics is considered for the object with the virtual input chosen as the solution of an optimal Linear Quadratic Tracking (LQT) problem. In this formulation, both the human and robots’ intentions are taken into account, being the former online estimated by Recursive Least Squares (RLS) technique. The output of this layer is a desired trajectory of the object which is the input of the bottom layer and from which desired trajectories for the robot end effectors are computed based on the closed-chain constraints. Each robot, then, implements a time-varying gain adaptive control law so as to take into account model uncertainty and internal wrenches that inevitably raise due to synchronization errors and dynamic and kinematic uncertainties. Remarkably, the overall solution is devised in a distributed setting by resorting to a leader-follower approach and distributed observers. Simulations with three 6-DOFs serial chain manipulators mounted on mobile platforms corroborate the theoretical findings.

OBAMA: OBAMA for Bayesian amino-acid model averaging.

BackgroundBayesian analyses offer many benefits for phylogenetic, and have been popular for analysis of amino acid alignments. It is necessary to specify a substitution and site model for such analyses, and often an ad hoc, or likelihood based method is employed for choosing these models that are typically of no interest to the analysis overall.MethodsWe present a method called OBAMA that averages over substitution models and site models, thus letting the data inform model choices and taking model uncertainty into account. It uses trans-dimensional Markov Chain Monte Carlo (MCMC) proposals to switch between various empirical substitution models for amino acids such as Dayhoff, WAG, and JTT. Furthermore, it switches base frequencies from these substitution models or use base frequencies estimated based on the alignment. Finally, it switches between using gamma rate heterogeneity or not, and between using a proportion of invariable sites or not.ResultsWe show that the model performs well in a simulation study. By using appropriate priors, we demonstrate both proportion of invariable sites and the shape parameter for gamma rate heterogeneity can be estimated. The OBAMA method allows taking in account model uncertainty, thus reducing bias in phylogenetic estimates. The method is implemented in the OBAMA package in BEAST 2, which is open source licensed under LGPL and allows joint tree inference under a wide range of models.

Design and Simulation of Graphical User interface for Cables Parallel Robot

In this paper, we have presented the 3D cables parallel robot in pyramid’s form. The dynamic equation has been established including the dynamic behavior, in this context; we investigated to use the Runge Kutta method of 4th order to solving non-linear partial differential equation of our system. The main contribution of this work is firstly: a graphical user interface (GUI) has been developed and implemented based on the geometric model, in order to visualizing the position of end effect or, taking account model uncertainties and payload variation. Secondly, we have studied and solved our system’s dynamical equation with implementation the PD control. This last, applied for different trajectories so as to test the accurate tracking the desired trajectory simulation using MATLAB software. As a result, the simulation tests on this robot verify the efficiency performance of the proposed controller.

The Recent Burstiness of Star Formation in Galaxies at z ∼ 4.5 from Hα Measurements

Abstract The redshift range z = 4–6 marks a transition phase between primordial and mature galaxy formation in which galaxies considerably increase their stellar mass, metallicity, and dust content. The study of galaxies in this redshift range is therefore important to understanding early galaxy formation and the fate of galaxies at later times. Here, we investigate the burstiness of the recent star formation history (SFH) of 221z ∼ 4.5 main-sequence galaxies at by comparing their ultra-violet (UV) continuum, Hα luminosity, and Hα equivalent-width (EW). The Hα properties are derived from the Spitzer [3.6 μm]−[4.5 μm] broadband color, thereby properly taking into account model and photometric uncertainties. We find a significant scatter between Hα- and UV-derived luminosities and star formation rates (SFRs). About half of the galaxies show a significant excess in Hα compared to expectations from a constant smooth SFH. We also find a tentative anticorrelation between Hα EW and stellar mass, ranging from 1000 Å at to below 100 Å at . Consulting models suggests that most z ∼ 4.5 galaxies had a burst of star formation within the last 50 Myr, increasing their SFRs by a factor of >5. The most massive galaxies on the other hand might decrease their SFRs and may be transitioning to a quiescent stage by z = 4. We identify differential dust attenuation (f) between stars and nebular regions as the main contributor to the uncertainty. With local galaxies selected by increasing Hα EW (reaching values similar to high-z galaxies), we predict that f approaches unity at z > 4, consistent with the extrapolation of measurements out to z = 2.

INFERNO: Inference-Aware Neural Optimisation

Complex computer simulations are commonly required for accurate data modelling in many scientific disciplines, making statistical inference challenging due to the intractability of the likelihood evaluation for the observed data. Furthermore, sometimes one is interested on inference drawn over a subset of the generative model parameters while taking into account model uncertainty or misspecification on the remaining nuisance parameters. In this work, we show how non-linear summary statistics can be constructed by minimising inference-motivated losses via stochastic gradient descent such that they provide the smallest uncertainty for the parameters of interest. As a use case, the problem of confidence interval estimation for the mixture coefficient in a multi-dimensional two-component mixture model (i.e. signal vs background) is considered, where the proposed technique clearly outperforms summary statistics based on probabilistic classification, a commonly used alternative which does not account for the presence of nuisance parameters.