Modeling the Viscoelastic Behavior of Quartz and Clay Minerals in Shale by Nanoindentation Creep Tests

The viscoelastic behavior of minerals in shales is important in predicting the macroscale creep behavior of heterogeneous bulk shale. In this study, in situ indentation measurements of two major constitutive minerals (i.e., quartz and clay) in Longmaxi Formation shale from the Sichuan Basin, South China, were conducted using a nanoindentation technique and high-resolution optical microscope. Firstly, quartz and clay minerals were identified under an optical microscope based on their morphology, surface features, reflection characteristics, particle shapes, and indentation responses. Three viscoelastic models (i.e., three-element Voigt, Burger’s, and two-dashpot Kelvin models) were then used to fit the creep data for both minerals. Finally, the effects of peak load on the viscoelastic behavior of quartz and clay minerals were investigated. Our results show that the sizes of the residual imprints on clay minerals were larger than that of quartz for a specific peak load. Moreover, the initial creep rates and depths in clay minerals were higher than those in quartz. However, the creep rates of quartz and clay minerals displayed similar trends, which were independent of peak load. In addition, all three viscoelastic models produced good fits to the experimental data. However, due to the poor fit in the initial holding stage of the three-element Voigt model and instability of the two-dashpot Kelvin model, Burger’s model is best in obtaining the regression parameters. The regression results indicate that the viscoelastic parameters obtained by these models are associated with peak load, and that a relatively small peak load is more reliable for the determination of viscoelastic parameters. Furthermore, the regression values for the viscoelastic parameters of clay minerals were lower than those of quartz and the bulk shale, suggesting the former facilitates the viscoelastic deformation of shale. Our study provides a better understanding of the nanoscale viscoelastic properties of shale, which can be used to predict the time-dependent deformation of shale.

Einstein-Roscoe Regression for the Slag Viscosity Prediction Problem in Steelmaking

In classical machine learning, regressors are trained without attempting to gain insight into the mechanism connecting inputs and outputs. Natural sciences, however, are interested in finding a robust interpretable function for the target phenomenon, that can return predictions even outside of the training domains. This paper focuses on viscosity prediction problem in steelmaking, and proposes Einstein-Roscoe regression (ERR), which learns the coefficients of the Einstein-Roscoe equation, and is able to extrapolate to unseen domains. Besides, it is often the case in the natural sciences that some measurements are much more expensive than the others due to physical constraints. To this end, we employ a transfer learning framework based on Gaussian process, which allows us to estimate the regression parameters using the auxiliary measurements available in a reasonable cost. In experiments using the viscosity measurements in high temperature slag system, ERR is compared favorably with various machine learning approaches in interpolation settings, while outperformed all of them in extrapolation settings. Furthermore, after estimating parameters using the auxiliary dataset obtained at room temperature, increase in accuracy is observed in the high temperature dataset, which corroborates the effectiveness of the proposed approach.

A Multi-Year Study of GOES-13 Droplet Effective Radius Retrievals for Warm Clouds over South America and Southeast Pacific

Geostationary satellites can retrieve the cloud droplet effective radius (re) but suffer biases from cloud inhomogeneities, internal retrieval nonlinearities, and 3-D scattering/shadowing from neighboring clouds, among others. A 1-D retrieval method was applied to Geostationary Operational Environmental Satellite 13 (GOES-13) imagery, over large areas in South America (+5∘ to −30∘ N5∘ N–30∘ S; −20∘ to −70∘E20∘–70∘ W), the Southeast Pacific (+5∘ to −30∘ N5∘ N–30∘ S; −70∘ to −120∘E70∘–120∘ W), and the Amazon (+2∘ to −7∘ N2∘ N–7∘ S; −54∘ to −73∘E54∘–73∘ W), for four months in each year from 2014–2017. Results were regressedcompared against in situ aircraft measurements and the Moderate Resolution Imaging Spectroradiometer cloud product for Terra and Aqua satellites. Monthly regression parameters approximately followed a seasonal pattern. With up to 108,009 of matchups, slope, intercept, and correlation for Terra (Aqua) ranged from about 0.71 to 1.17, −2.8 to 2.5 μm, and 0.61 to 0.91 (0.54 to 0.78, −1.5 to 1.8 μm, 0.63 to 0.89), respectively. We identified evidence for re overestimation (underestimation) correlated with shadowing (enhanced reflectance) in the forward (backscattering) hemisphere, and limitations to illumination/ and viewing configurations accessible by GOES-13, depending on the time of day and season. A proposition is hypothesized to ameliorate 3-D biases by studying relative illumination and cloud spatial inhomogeneity.

Fisher consistent estimation of regression parameter in the proportional mean residual life model with frailty

In the article, we consider the Fisher consistent estimation of the regression parameters in the proportional mean residual life model with arbitrary frailty. It is discussed that conventional estimation procedures, such as the maximum likelihood estimation or Cox’s approach, which are employed in common regression models, may also yield consistent inference in the extended models.

PEMODELAN REGRESI RIDGE ROBUST S,M, MM-ESTIMATOR DALAM PENANGANAN MULTIKOLINIERITAS DAN PENCILAN (Studi Kasus : Faktor-Faktor yang Mempengaruhi Kemiskinan di Jawa Tengah Tahun 2020)

The Ordinary Least Squares (OLS) is one of the most commonly used method to estimate linier regression parameters. If there is a violation of assumptions such as multicolliniearity especially coupled with the outliers, then the regression with OLS is no longer used. One method can be used to solved the multicollinearity and outliers problem is Ridge Robust Regression. Ridge Robust Regression is a modification of ridge regression method used to solve the multicolliniearity and using some estimators of robust regression used to solve the outlier, the estimator including : Maximum likelihood estimator (M-estimator), Scale estimator (S-estimator), and Method of moment estimator (MM-estimator). The case study can be used with this method is data with multicollinearity and outlier, the case study in this research is poverty in Central Java 2020 influenced by life expentancy, unemployment number, GRDP rate, dependency ratio, human development index, the precentage of population over 15 years of age with the highest education in primary school, mean years school. The result of estimation using OLS show that there is a multicollinearity and presence an outliers. Applied the ridge robust regression to case study prove that ridge robust regression can improve parameter estimation. The best ridge robust regression model is Ridge Robust Regression S-Estimator. The influence value of predictor variabels to poverty is 73,08% and the MSE value is 0,00791.

Bayesian Regression of Government Expenditure on Revenue in Nigeria

The relationship between government expenditure and its revenue is generating serious debate among researchers. Similarly, their has been a controversy between the classical and the bayesian modelling. Therfore, this study examined the relationship between the government expenditure and its revenue in Nigeria using the bayesian approach. The finance data extracted from the Central Bank of Nigeria statistical bulletin from 1989 to 2018 were considered for the study. Bayesian linear regression was used to fit the model. Normal distribution was fit for the likelihood. Thus, normal-gamma prior was elicited for the bayesian regression parameters. The result showed that the Bayesian estimates with elicited normal-gamma prior produced a better posterior mean of 0.536 for the Total Revenue with a smaller posterior standard deviation of 0.00001 when compared with the OLS standard deviation of 0.05256. Similarly, the total revenue explained 78% variations in the Total expenditure. The constructed model fit was: Total Expenditure = 98.57128 + 0.53630* Total Revenue. This showed that a naira unit of the total expenditure will always be increased by 0.54 of the total revenue. Forecast of 30 years for the total expenditure using both OLS and Bayesian (normal gamma prior) were increasing as the years were progressing. Government should look for a way to increase its revenue in order to sustain the future expenses of the government since expenditure increases yearly.

Modeling eutrophication risks in Tanes reservoir by using a hybrid WOA optimized SVR-relied technique along with feature selection based on the MARS approximation

Total phosphorus (from now on mentioned as TP) and chlorophyll-a (from now on mentioned as Chl-a) are recognized indicators for phytoplankton large quantity and biomass-thus, actual estimates of the eutrophic state-of water bodies (i.e., reservoirs, lakes and seas). A robust nonparametric method, called support vector regression (SVR) approach, for forecasting the output Chl-a and TP concentrations coming from 268 samples obtained in Tanes reservoir is described in this investigation. Previously, we have carried out a selection of the main features (biological and physico-chemical predictors) employing the multivariate adaptive regression splines approximation to construct reduced models for the purpose of making them easier to interpret for researchers/readers and to reduce the overfitting. As an optimizer, the heuristic technique termed as whale optimization iterative algorithm (WOA), was employed here to optimize the regression parameters with success. Two main results have been obtained. Firstly, the relative relevance of the models variables was stablished. Secondly, the Chl-a and TP can be successfully foretold employing this hybrid WOA/SVR-based approximation. The coincidence between the predicted approximation and the observed data obviously demonstrates the quality of this novel technique.

Regression with Stable Errors Based on Order Statistics

Classical regression approaches are not robust when errors are heavy-tailed or asymmetric. That may be due to the non-existence of the mean or variance of the error distribution. Estimation based on trimmed data, which ignored outlier or leverage points, has an old history and frequently used. This procedure chooses fixed cut-off points. In this work, we use this idea recently applied for initial estimates of regression coefficients with heavy-tailed stable errors. We propose an effective procedure to calculate the cut-off points based on the tail index and skewness parameters of errors. We use the property of the existence of some moments of stable distribution order statistics. Data are trimmed based on ordered residuals of a least square regression. However, the trimmed data’s optimal number is determined based on the number of error order statistics whose variance exists. Then, we use the rest of the ordered data to estimate the regression coefficients. Based on these order statistics’ joint distribution, we analytically compute the bias and variance of the introduced estimator of regression parameters that was impossible for regression with stable errors.

The effect of patient weight and amiodarone use on the energy of a successful shock in cardioversion of atrial arrhythmias – single-center experience study

Background Electrical cardioversion (DCCV) is an effective method of sinus rhythm restitution. Recently published data suggest higher efficiencies of higher discharge energies. The influence of individual parameters on the success of cardioversion is still studying. Purpose To evaluate the influence of individual patient parameters on the energy of a successful external defibrillator shock during cardioversion of atrial arrhythmias Methods The retrospective analysis cohort of all patients treated by DCCV due to atrial arrhythmias between 10/2015 and 1/2020. To evaluate potential predictors for the choice of a higher initial discharge using one-dimensional logistic regression and to include parameters significant at the 10% level of significance (p<0.1) in the multidimensional logistic regression model. Results 1986 electrical cardioversions of 984 patients (382 repeated procedures of the same patients in a cohort). 1292 (65.1%) men and 694 (34.9%) women aged 67.0 (±10.2) years. Mean energy of the first shock 118.9 (±19.2) J with a success rate of 77.8%, energy of the second shock 154.0 (±26.3) J, which increased the overall success to 80.0%, and mean high of the third shock 173.9 (±25.6) J, when the total efficacy of DCCV in sinus rhytm restitution reached 89.8%. From the univariate binary regression, parameters significant at the 10% level of significance (p<0.1) were selected and included in a multidimensional logistic regression model. Only the patient's weight and the use of amiodarone proved to be statistically significant. Weight with OR 1.21 and use of amiodarone with OR 1.43. Conclusion Patient weight and amiodarone use are predictors of the need to use higher energy electric cardioversion. Discussion Amiodarone medication does not appear to increase the defibrillation threshold, but rather is a feature that represents the group of patients treated with more aggressive antiarrhythmic therapy for advanced atrial arrhythmias with more significant structural impairment, but this attitude requires further study. FUNDunding Acknowledgement Type of funding sources: Private hospital(s). Main funding source(s): VAVIA: IGS202009 - Racionální algoritmus při elektivní elektrické kardioverzi fibrilace síní.

Microstructure modeling of a sintered Al–4Si–0.6Mg alloy extruded at semi-solid temperature ranges

The microstructure evolution of sintered and extruded samples of Al–4Si–0.6Mg powder alloys at various semi-solid temperature ranges of 560 °C, 580 °C, and 600 °C, holding times of 600, 1200, and 1800 s, and strain rates of 0.1, 0.2, and 0.3 s−1 was studied. From the stress–strain curves and metallographic studies, Arrhenius grain growth model and Avrami dynamic recrystallization model have been formulated by means of linear regression. Parameters such as peak strain, critical strain, recrystallization fraction, and material constants have been found using the above equations. The experimental and calculated values of various material parameters agree with each other, indicating the accuracy of the developed model. Finite element method-based simulations were performed using DEFORM 2D software, and the average grain size obtained from experiments and simulations was validated by means of average grain size. The relative density of the compacted specimens as well as the extruded specimens was also simulated. The simulation results showed that large grains appeared at high temperatures and at the bottom of the specimen.