Sequential Sampling Method Research Articles

Assimilation of probabilistic flood maps from SAR data into a coupled hydrologic–hydraulic forecasting model: a proof of concept

Abstract. Coupled hydrologic and hydraulic models represent powerful tools for simulating streamflow and water levels along the riverbed and in the floodplain. However, input data, model parameters, initial conditions, and model structure represent sources of uncertainty that affect the reliability and accuracy of flood forecasts. Assimilation of satellite-based synthetic aperture radar (SAR) observations into a flood forecasting model is generally used to reduce such uncertainties. In this context, we have evaluated how sequential assimilation of flood extent derived from SAR data can help improve flood forecasts. In particular, we carried out twin experiments based on a synthetically generated dataset with controlled uncertainty. To this end, two assimilation methods are explored and compared: the sequential importance sampling method (standard method) and its enhanced method where a tempering coefficient is used to inflate the posterior probability (adapted method) and reduce degeneracy. The experimental results show that the assimilation of SAR probabilistic flood maps significantly improves the predictions of streamflow and water elevation, thereby confirming the effectiveness of the data assimilation framework. In addition, the assimilation method significantly reduces the spatially averaged root mean square error of water levels with respect to the case without assimilation. The critical success index of predicted flood extent maps is significantly increased by the assimilation. While the standard method proves to be more accurate in estimating the water levels and streamflow at the assimilation time step, the adapted method enables a more persistent improvement of the forecasts. However, although the use of a tempering coefficient reduces the degeneracy problem, the accuracy of model simulation is lower than that of the standard method at the assimilation time step.

Global EM-driven optimization of multi-band antennas using knowledge-based inverse response-feature surrogates

Electromagnetic simulation tools have been playing an increasing role in the design of contemporary antenna structures. The employment of electromagnetic analysis ensures reliability of evaluating antenna characteristics but also incurs considerable computational expenses whenever massive simulations are involved (e.g., parametric optimization, uncertainty quantification). This high cost is the most serious bottleneck of simulation-driven design procedures, and may be troublesome even for local tuning of geometry parameters, let alone global optimization. On the one hand, globalized search is often necessary because the design problem might be multimodal (i.e., the objective function features multiple local optima) or a reasonably good initial design may not be available. On the other hand, the computational efficiency of popular algorithmic approaches, primarily, nature-inspired population-based algorithms, is generally poor. Combining metaheuristics procedures with surrogate modelling techniques and sequential sampling methods alleviates the problem to a certain extent but modelling of nonlinear antenna responses over broad frequency ranges is extremely challenging, and the aforementioned solutions are normally limited to rather simple structures described by a few parameters. This paper proposes a novel approach to global optimization of multi-band antennas. The major component of the presented framework is the knowledge-based inverse surrogate constructed at the level of response features (e.g., frequency and level locations of the antenna resonances). The surrogate facilitates decision-making process of inexpensive identification of the most promising regions of the parameter space, and a rendition of the good-quality initial design for further local tuning. Our methodology is validated using three examples of dual- and triple-band antennas. The average optimization cost is only 150 full-wave antenna analyses while ensuring precise allocation of the antenna resonances at the target frequencies. This performance is demonstrated superior over both local optimizers and population-based metaheuristics.

Global–Local Collaborative Optimization Method for Stiffened Cylindrical Shells with Cutout

In the cutout reinforcement for a stiffened cylindrical shell, the layout optimization of global stiffeners and the stiffeners around the cutout are generally separated. However, considering the two optimizations separately is noncollaborative. To solve this problem, a global–local collaborative optimization method is proposed. Based on the sequential sampling method, a set of sampling points is obtained in the design space. Local optimization is carried out to optimize the stiffener layout around the cutout for each sampling point along with its strain energy and stress. The radial basis function surrogate model is constructed based on the aforementioned sampling points. Based on the surrogate model, global optimization is carried out to obtain the optimal global stiffener layout and the stiffener layout around the cutout. In this way, the layout of the global stiffener and the layout of the local stiffener can be designed collaboratively. An illustrative example of the stiffened cylindrical shell with the cutout is carried out to verify the effectiveness of the proposed method. An innovative stiffened cylindrical shell with the cutout is obtained by a collaborative optimization method. The weight of the innovative design is reduced by 8.70%, and the maximum von Mises stress is reduced by 14.47% when compared with the optimal design by a noncollaborative optimization method.

Multiscale analysis and process parameters optimization of residual stress/strain of 3D woven composite

To predict the process-induced residual stress/strain of 3D woven composite and propose the optimal cure cycle, a multiscale model of the process analysis has been developed. Based on the representative volume elements (RVE) at the fiber and yarn scale, the modulus development of yarns and 3D woven composite was obtained. A thermal-chemical-mechanical coupling analysis was conducted on the yarn scale with the consideration of chemical shrinkage effect of resin, and the evolution of the microscopic stress-strain was calculated. The fiber Bragg grating (FBG) sensors were embedded in the 3D woven preform through the 3D weaving technique, and the evolutions of temperature and strain were monitored. The accuracy of the finite element model was validated by the experimental result. Three sequential sampling methods based on space, error and result were adopted to establish the surrogate model of the process analysis of 3D woven composite. Based on the surrogate model, the optimization of process parameters of 3D woven composite forming process was carried out. The results show that the residual strain is reduced by 15.4% and the cure cycle is shortened by 10.6%.

A new sequential sampling method of surrogate models for design and optimization of dynamic systems

When solving combined plant and control optimization (co-design) problems of actual dynamic systems, the models with computationally expensive states derivative functions will often be encountered. Jacobian information cannot be effectively extracted in the optimization process since the expressions of these models are extremely verbose or models are given in the form of simulation programs, which makes gradient-based optimization algorithms difficult. Then the alternative approaches based on finite difference technique are selected. These methods require numerous original expensive evaluations, hence co-design optimizations of these complicated systems are basically not impractical. Here, we propose a new sequential sampling strategy based on error filtering and distance clustering to construct and update surrogate models. The computational cost is significantly reduced through deploying these cheap surrogate models and their easy-to-extract Jacobian information. Dynamic optimization examples of the 2 DOF and 3 DOF robot illustrate the comparison of several sampling strategies and show the feasibility and efficiency of the proposed method. The co-design example of a wind turbine is followed to demonstrate its application prospect in engineering designs.

Sequential sampling method for monitoring potato tuber moths (Phthorimaea operculella) in potato fields

Sequential sampling method for monitoring potato tuber moths (Phthorimaea operculella) in potato fields

A Comparative Study on Adverse Pregnancy Outcomes in Pregnant Women with Different Age

Introduction: Delayed childbearing is increasing. Increased maternal age has been associated with increased risk of pregnancy complications. On the other hand, the adolescent pregnancy has traditionally been considered high-risk, especially in developing countries. Objective: This study aimed to compare the adverse pregnancy outcomes in different age groups of pregnant women. Materials and Methods: The analytical study with cross-sectional design was conducted on 345 pregnant women referred to one of the maternity hospitals in Rasht, Iran in 2018 who were selected by sequential sampling method. They were followed from gestational age >37 weeks until delivery in 3 groups: <19 years, 19-35 years and >35 years. The data collection tool was a two-part researcher-made questionnaire which surveys demographic, fertility, and socioeconomic information and adverse maternal and neonatal outcomes. The collected data were analyzed using descriptive and inferential statistics (Chi-square test, Fisher’s exact test, Kruskal-Wallis test, ANOVA, Bonfrroni test, Spearman correlation test, and multiple linear regression analysis). Results: The highest prevalence of maternal diabetes mellitus (29.7%) and cesarean section (72.46%) were reported in the age group >35 years. The highest frequency of premature rupture of membranes (18.8%) and abnormal fetal presentation (13.8 %) were reported in age groups <19 years and 19-35 years, respectively. Among maternal outcomes, maternal diabetes, abnormal fetal presentation, premature rupture of membranes and delivery by cesarean section were significantly different between three groups (P<0.05). There was a significant difference between maternal age and neonatal outcomes in ages groups <19 years and 19-35 years (P<0.05). Multiple linear regression model showed that the maternal age <19 years was a predictor of adverse neonatal outcomes. The group <19 years reported more adverse outcomes than the age group 19-35 years (β=0.282, 95% CL: 0.002-0.561, P=0.049). Conclusion: All age groups of pregnant women faced some pregnancy outcomes. Due to Iran’s new plan to encourage childbearing, prenatal screening, and receiving more prenatal care, we recommend pregnant women to participate in childbirth preparation training courses.

An efficient global reliability analysis method for turbine disc’s multi-failure modes considering importance distribution technology

This paper presents an improved efficient global reliability analysis (EGRA) method for a turbine disc considering the importance distribution of multi-failure modes, such as low cycle fatigue (LCF) and creep-fatigue (CF). Failure mode effects and criticality analysis (FMECA) technology is firstly used to analyse the criticality and occurrence probability of each failure mode. Importance factor S is then proposed to quantify the importance of the corresponding failure mode. In order to improve the reliability analysis’s efficiency, expected feasibility function (EFF) criterion in sequential sampling method is selected through numerical experiments to generate the samples of random variables. Secondly, EGRA method is established by combining sampling method with Kriging model, in which the performance function is fitted using the key sampling points near both the failure boundaries and high uncertainty region. On the basis of EFF criterion, the composite EFF criterion applied to EGRA method is developed. This method avoids repeated analysis on non-significant failure modes, which further improves the reliability analysis’s efficiency for the multi-failure modes system. Through a binary nonlinear example, it is proved that the prediction accuracy is 99.8% compared with Monte Carlo method. Moreover, the computational time of reliability is reduced by half compared with EFF criterion, which proves the prediction efficiency of composite EFF criterion. Finally, the reliability analysis on a turbine disc is conducted using the improved EGRA method combining with the failure mode importance distribution technology. Results show that the fatigue life at 99.9% reliability is decreased by 7.4% after considering the importance of failure modes, indicating that the ignoration of failure modes’ importance would lead to a more dangerous prediction result.

Remdesivir versus Standard of Care in Moderate to Severe COVID-19 Patients: A Retrospective Study

Introduction: In December 2019, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), a novel coronavirus, was first identified as the cause of a respiratory illness designated Coronavirus Disease 2019 (COVID-19). Since then, several anti-viral drugs have been evaluated for the treatment of COVID-19, but none have shown any efficacy. The only drug which showed some efficacy was Remdesivir (RDV). Aim: To assess the effect and efficacy of RDV and to compare the outcome of patients who are receiving RDV and those receiving standard treatment protocol without RDV. Materials and Methods: A retrospective study was conducted. The data was collected from the case sheets of the case files of patients presenting to the Department of General Medicine Triage who were admitted from the month of July 2020 to December 2020 and analysis was done in January 2021. The method of sampling employed was a non-probability sequential sampling method. Results: Age and sex distributions were comparable in both the groups. The percentage of the patients who expired was 10% and 9% (n=10 and 9, respectively) in Non-RDV and RDV groups respectively, this was statistically insignificant (0.809).However, the duration of hospital stay in those who received RDV was 10 (9-12) days while those receiving standard of care without RDV was 12 (10-15) days (0.0018) which was statistically significant. Also, after a comparison between the two groups it was evident that there was a significant difference in inflammatory markers D-dimer and Lactate Dehydrogenase (LDH) with p=0.001 and 0.029, respectively. Conclusion: Study concludes that there was no significant difference in outcome of patients who received RDV. However, the duration of hospital stay was found to be decreased in patients receiving RDV and also there was a significant improvement in inflammatory markers like LDH and D-dimer.

The Effects of Inhalation Aromatherapy on Postoperative Abdominal Pain: A Three-Arm Randomized Controlled Clinical Trial

PurposeThis study compares the effects of inhalation aromatherapy using essential oils of sweet orange and damask rose on postoperative abdominal pain. DesignA randomized three-arm controlled trial. MethodsIn this randomized double-blinded, and parallel-group controlled trial, a total of 120 patients who underwent open abdominal surgeries were enrolled using a sequential sampling method. Participants were then randomly assigned to three groups of sweet orange, damask rose, and placebo (distilled water) using the permuted block randomization. When the patients regained full consciousness, a clean gauze impregnated with four drops of either distilled water or essential oils of sweet orange or damask rose were attached to the collar of the patients, and they were asked to inhale the aroma through normal breathing for 30 minutes. Abdominal pain severity was recorded using the visual analog scale at four time points including before the intervention (baseline) and 4, 8, and 12 hours after the intervention. FindingsPain reduction after sweet orange inhalation was significantly greater than placebo (at 8 and 12 hours after the intervention) and damask rose (at 12 hours after the intervention). The differences in the mean score of pain severity between all before-and-after observations were statistically significant in the three groups, except in the placebo group between the baseline score of pain severity and the pain severity score at 4 hours after the intervention. ConclusionsInhalation aromatherapy using sweet orange seems to be more effective than the damask rose in reducing pain severity after open abdominal surgeries.

First quantitative assessment of growth, sugar accumulation and malate breakdown in a single ripening berry

Background: Most approaches to grape physiology accept that the berry and the future harvest should display identical developmental features, which obviously requires synchronised fruits.Aims: Rejecting this assumption compels to revisit the kinetic and metabolic bases of berry ripening.Methods and Results: Two to three thousand berries were individually analysed for sugar, malate and weight. The huge heterogeneity in sugar and malic acid concentrations among fruits was mostly explained by time lags in the onset of sugar storage, which proved nearly as long as the second growth phase. Individual berries from different cultivars displayed similar kinetics following the normalisation of their maximal volume. Phloem sucrose unloading started at its maximum speed at softening, but growth resumed one week later. Four hexoses accumulated per malic acid, which was oxidised during the first two weeks of ripening, and then malate breakdown stopped without affecting sugar accumulation. Sugar and water accumulation were simultaneously arrested four weeks after softening, at 0.9 M hexose, at which point sugar concentration continued through water losses.Conclusions: The accepted sequential random sampling methods representative of average fruit and future wine compositions have led to a scrambled vision of grape developmental biology, presenting serious kinetic and composition biases. Single berry composition provides first quantitative evidence for the induction of a dominant H+/sucrose exchange on the tonoplast, which is first electro-neutralised by malate breakdown, then by ATP demanding H+ recirculation, in line with functional and molecular studies.Significance of the Study: The kinetics of single berry ripening are presented for the first time. A more reliable and reproducible model of berry growth, sugar import and malate breakdown is shown here, which have definitively been improved from a quantitative point of view. It illustrates that the temporal structure of a berry population may largely contribute to future wine quality, in addition to metabolic plasticity, thereby providing another target for the impact of GxE interaction. In this respect, addressing the structure of berry cohorts may provide a new approach regarding the developmental biology/terroir nexus.

Efficient high-dimensional material reliability analysis with explicit voxel-level stochastic microstructure representation

A novel efficient methodology for probabilistic material reliability analysis considering fine-scale microstructure stochasticity is proposed in this paper. Integrated computational material engineering requires efficient multiscale computational capabilities to enable computational design and validation. Two critical challenges are identified: handling uncertainties from microstructures and material properties; and handling the “curse of dimensionality” for probabilistic solvers. The proposed study addresses these two critical challenges. First, an analytical and hierarchical uncertainty quantification method is proposed for the explicit stochastic microstructure representation at the voxel-level. The hierarchy of uncertainties from both phase maps and uncertainties within each phase is modeled using an explicit Gaussian mixture random field. Analytical approximation for the arbitrary non-Gaussian random field is derived, which can facilitate the computation of gradient information in optimization. Following this, an efficient probabilistic solver using adjoint first-order reliability method combining the importance sampling is derived by formulating the material reliability analysis as a constrained optimization problem. The adjoint method is used to efficiently evaluate the responses and exact gradients with the help of the analytical Gaussian mixture random field. Several numerical examples for material reliability calculation with high-dimensional (voxel-level) random fields are subsequently employed to demonstrate and validate the proposed methodology. The results of the proposed method are quantitatively compared to those obtained via the classical first-order reliability method, direct Monte Carlo simulation, subset simulation, and the sequential importance sampling method. The comparisons indicate that the proposed method possesses high efficiency for high-dimensional material reliability problems.

A combined radial basis function and adaptive sequential sampling method for structural reliability analysis

In this paper, according to the Kriging based reliability analysis method, an efficient sequential sampling method combined with radial basis function is proposed to reduce the modeling complexity of the surrogate model and eliminate the uncertainties of the Kriging itself on the reliability analysis results. A novel active learning function is developed that can search for the sequential samples effectively among the candidate set. For terminating the sequential sampling process, a corresponding convergence criterion according to the failure probability obtained from the cross-validation method is constructed. Furthermore, the proposed method can be applied to any other surrogate model in principle. Five numerical examples demonstrate that the proposed method has high precision and efficiency as well as strong applicability in structural reliability analysis.

Efficient high-dimensional metamodeling strategy using recursive decomposition coupled with sequential sampling method

Metamodel has been widely used to solve computationally expensive engineering problems, and there have been many studies on how to efficiently and accurately generate metamodels with limited number of samples. However, applications of these methods could be limited in high-dimensional problems since it is still challenging due to curse of dimensionality to generate accurate metamodels in high-dimensional design space. In this paper, recursive decomposition coupled with a sequential sampling method is proposed to identify latent decomposability and efficiently generate high-dimensional metamodels. Whenever a new sample is inserted, variable decomposition is repeatedly performed using interaction estimation from a full-dimension Kriging metamodel. The sampling strategy of the proposed method consists of two units: decomposition unit and accuracy improvement unit. Using the proposed method, latent decomposability of a function can be identified using reasonable number of samples, and a high-dimensional metamodel can be generated very efficiently and accurately using the identified decomposability. Numerical examples using both decomposable and indecomposable problems show that the proposed method shows reasonable decomposition results, and thus improves metamodel accuracy using similar number of samples compared with conventional methods.

Quantifying time-varying sources in magnetoencephalography—A discrete approach

We study the distribution of brain source from the most advanced brain imaging technique, Magnetoencephalography (MEG) which measures the magnetic fields outside of the human head produced by the electrical activity inside the brain. Common time-varying source localization methods assume the source current with a time-varying structure and solve the MEG inverse problem by mainly estimating the source moment parameters. These methods use the fact that the magnetic fields linearly depend on the moment parameters of the source and work well under the linear dynamic system. However, magnetic fields are known to be nonlinearly related to the location parameters of the source. The existing work on estimating the time-varying unknown location parameters is limited. We are motivated to investigate the source distribution for the location parameters based on a dynamic framework, where the posterior distribution of the source is computed in a closed form discretely. The new framework allows us not only to directly approximate the posterior distribution of the source current, where sequential sampling methods may suffer from slow convergence due to the large volume of measurement, but also to quantify the source distribution at any time point from the entire set of measurements reflecting the distribution of the source, rather than using only the measurements up to the time point of interest. Both a dynamic procedure and a switch procedure are pro- posed for the new discrete approach, balancing estimation accuracy and computational efficiency when multiple sources are present. In both simulation and real data, we illustrate that the new method is able to provide comprehensive insight into the time evolution of the sources at different stages of the MEG and EEG experiment.

Fast-sequential determination of cadmium and copper in milk powder and infant formula by direct solid sampling high-resolution continuum source graphite furnace atomic absorption spectrometry

A sequential and direct solid sampling (SS) method to determine cadmium (Cd) and copper (Cu) in milk powders and infant formulas was developed using a high-resolution continuum source graphite furnace atomic absorption (HR-CS GFAA) spectrometer. The analysis was performed using a unique sample aliquot and in one measurement run. Absorbance measurements were carried out at the main analytical lines for Cd (228.802 nm) and Cu (324.754 nm). Experimental conditions were optimized using aqueous standards and a skimmed milk powder certified reference material (ERM®–BD150). Palladium was added as chemical modifier in standards and samples. For Cd determination, a pyrolysis temperature of 900 °C and an atomization temperature of 1400 °C were used. Afterwards, the wavelength was automatically changed, and Cu was determined from the same aliquot of sample using an atomization temperature of 2200 °C. Calibration was performed using aqueous standards. Using three pixels (CP ± 1) for the quantification of both elements and a sample mass of 0.9 mg, the limits of quantification (LOQs) and the upper limits of the linear range (ULLRs) were 2.1 µg kg−1 and 56 µg kg−1 for Cd (1.9 to 50 pg), and 0.021 mg kg−1 and 1.7 mg kg−1 for Cu (0.019 to 1.5 ng), respectively. The linear response of Cu was expanded up to 4.7 mg kg−1 (4.2 ng) by using the approach of measuring at the wings of the absorption line. The developed methodology was applied to analyse different commercial milk powder and infant formula samples with successful results.

Parameter Estimation of Acoustic Wave Equations Using Hidden Physics Models

In this article, we present one numerical approach to infer the model parameters and state variables of acoustic wave equations. The method we consider is based on the recently proposed method-the so-called hidden physics model. With placing Gaussian process (GP) prior on the state variables, the structure and model parameters of acoustic wave equations are encoded into the kernel function of a multioutput GP. The purpose of this article includes: 1) testing the applicability of hidden physics model to infer the velocity, density, and state variables of the acoustic wave equation, which is important for many applications in geophysics; 2) adapting the method to handle for both homogeneous and heterogeneous media that are of practical interest; and 3) exploring efficient sequential sampling methods to improve the sampling efficiency. We suggest that the expected-improvement-based sequential sampling method would be effective for most practical problems related to acoustic wave propagation. Besides, we demonstrate the performance of the proposed scheme via several benchmark problems.

Sequential sampling method using Gaussian process regression for estimating extreme structural response

A methodology for estimating extreme response statistics for marine structures, that takes both the long-term variability of the metocean environment and the short-term variability of response into account is presented. The proposed methodology uses Gaussian process regression to estimate parameters of the short-term response distribution, based on output from computationally expensive hydrodynamic simulations. We present an adaptive design strategy for sequential updating of the model, focusing on the metocean conditions that contribute the most to the long-term extreme. With this approach, only a limited number of hydrodynamic simulations are needed.The suggested approach is demonstrated on the problem of estimating the 25-year extreme vertical bending moment on a ship. We show that a relatively small number of iterations (full hydrodynamic simulations) are needed to converge toward the “exact” results obtained by running a large number of simulations covering the entire range of sea states.The results suggest that the proposed method can be used as an alternative to contour-based methods or other methods that consider a few sea states using accurate numerical simulations, with little or no added complexity or computational effort.

A Sequential Importance Sampling Algorithm for Counting Linear Extensions

In recent decades, a number of profound theorems concerning approximation of hard counting problems have appeared. These include estimation of the permanent, estimating the volume of a convex polyhedron, and counting (approximately) the number of linear extensions of a partially ordered set. All of these results have been achieved using probabilistic sampling methods, specifically Monte Carlo Markov Chain (MCMC) techniques. In each case, a rapidly mixing Markov chain is defined that is guaranteed to produce, with high probability, an accurate result after only a polynomial number of operations. Although of polynomial complexity, none of these results lead to a practical computational technique, nor do they claim to. The polynomials are of high degree and a non-trivial amount of computing is required to get even a single sample. Our aim in this article is to present practical Monte Carlo methods for one of these problems, counting linear extensions. Like related work on estimating the coefficients of the reliability polynomial, our technique is based on improving the so-called Knuth counting algorithm by incorporating an importance function into the node selection technique giving a sequential importance sampling (SIS) method. We define and report performance on two importance functions.

A multi-criteria adaptive sequential sampling method for radial basis function

A multi-criteria adaptive sequential sampling method for radial basis function