On the Performance of Isotropic Hyperelastic Constitutive Models for Rubber-Like Materials: A State of the Art Review

Besides the well-known landmark models for hyperelastic response of rubberlike materials, many new hyperelastic constitutive models heve emerged over the last decade. Despite many reviews on constitutive modelling or elastomers, it is still a challenging endeavor for engineers to decide for a constitutive model for the specific rubber compound and application. In this work, we have reviewed 44 hyperelastic constitutive models for elastomers and assessed their strength and weaknesses under uniaxial, pure shear, and (equi)biaxial deformations. To this end, we first present a novel parameter identification methodology based on various multi-objective optimization strategies for the selection of the best constitutive models from a given set of uniaxial tension, pure shear and (equi)biaxial tension experiments. We utilize a hybrid multi-objective optimization procedure using a genetic algorithm to generate multiple initial points for gradient based search algorithm, Fmincon utility in Matlab. The novelty of the approach is (i) simultaneous fitting with variable weight factors for uniaxial, equibiaxial, and pure shear data, and (ii) the sorting of the models based on an objective normalized quality of fit metric. For the models incapable of simultaneously fitting the three distinct deformation data, the validity range is assessed through a threshold value for the quality of fit measure. Accordingly, 44 hyperelastic models are sorted with respect to their simultaneous fitting performance to the experimental dataset of Treloar and Kawabata. Based on the number of material parameters, and their fitting performance to experimental data, a detailed discussion is carried out.

The composite face effect is robust against perceptual misfit

The composite face effect—the failure of selective attention toward a target face half—is frequently used to study mechanisms of feature integration in faces. Here we studied how this effect depends on the perceptual fit between attended and unattended halves. We used composite faces that were rated by trained observers as either a seamless fit (i.e., close to a natural and homogeneous face) or as a deliberately bad quality of fit (i.e., unnatural, strongly segregated face halves). In addition, composites created by combining face halves randomly were tested. The composite face effect was measured as the alignment × congruency interaction (Gauthier and Bukach Cognition, 103, 322–330 2007), but also with alternative data analysis procedures (Rossion and Boremanse Journal of Vision, 8, 1–13 2008). We found strong but identical composite effects in all fit conditions. Fit quality neither increased the composite face effect nor was it attenuated by bad or random fit quality. The implications for a Gestalt account of holistic face processing are discussed.

DEVELOPING OF MATLAB APPLICATION FOR DETERMINING THE REACTION KINETICS OF RUBBER VULCANIZATION PROCESS

Rubber vulcanization is kinetically a complex process, since it consists of two simultaneous reactions: curing and degradation. To determine reaction kinetics, it is necessary to determine a kinetic model which describes the process adequately. Proposed kinetic model has six adjustable parameters. In order to determine kinetic parameters of the proposed kinetic model, commercially available rubber gum was used. Oscillating disc rheometer was used to investigate experimental dependence of torque on time, at six temperatures in the range from 130 to 180 °C, with a step of 10 °C. Matlab application, built via App Designer feature, was developed in order to fit the experimental data to the proposed kinetic model. Developed Matlab application, consisting of two tabs, enables user to upload raw rheometer data, perform manual fitting or automatic fitting (manual or automatic estimation of initial values of adjustable parameters), test the effect of constant values of some kinetic parameters on the overall quality of fit, visualize the dependence of kinetic parameters on temperature and to determine the values of Arrhenius expression for curing and degradation process. Both fitting methods were proven to be efficient; overall determination coefficient and MAPE value for automatic and manual fitting methods were >0.99 and <1%, and >0.999 and <1%, respectively. Arrhenius parameters were also determined with high accuracy (R2>0.98). Developed application enables simple and efficient determination of kinetic parameters by means of different fitting methods, simultaneous fitting of data on all temperatures, and testing the effect of constant kinetic parameters values on fitting results

Mathematical Modeling for Determination of Correlation Between Current Density and Dissolved Oxygen in Yeast Microbial Fuel Cell-Based Biosensor

Experiments were conducted to study the correlation between current density and dissolved oxygen (DO) and to develop a model for estimating the value of current density in yeast MFC based DO biosensors. A curve between current density and DO was made, and data analysis was performed using free-online data fitting, namely zunzun.com. One linear regression and nine different exponential models are used as an approach to determine the correlation between current density and DO. The higher DO, the current density will increase rapidly. The most suitable model was chosen to describe the correlation between the current density and the DO. The coefficient of determination (R2), the sum of square absolute (SSQABS), and root mean square error (RMSE) are used to determine goodness or quality of fit. The exponential model shows a better fit to illustrate the correlation between current density and DO, with R2, SSQABS, and RMSE values were 0.9975, 0.4745 and 0.3444, respectively.

Multi-Class Imbalanced Graph Convolutional Network Learning

Networked data often demonstrate the Pareto principle (i.e., 80/20 rule) with skewed class distributions, where most vertices belong to a few majority classes and minority classes only contain a handful of instances. When presented with imbalanced class distributions, existing graph embedding learning tends to bias to nodes from majority classes, leaving nodes from minority classes under-trained. In this paper, we propose Dual-Regularized Graph Convolutional Networks (DR-GCN) to handle multi-class imbalanced graphs, where two types of regularization are imposed to tackle class imbalanced representation learning. To ensure that all classes are equally represented, we propose a class-conditioned adversarial training process to facilitate the separation of labeled nodes. Meanwhile, to maintain training equilibrium (i.e., retaining quality of fit across all classes), we force unlabeled nodes to follow a similar latent distribution to the labeled nodes by minimizing their difference in the embedding space. Experiments on real-world imbalanced graphs demonstrate that DR-GCN outperforms the state-of-the-art methods in node classification, graph clustering, and visualization.

A simplex dispersion model for improving precision in the odds ratio confidence interval in mixture experiments

A new approach to data analysis in mixture experiments is proposed using the simplex regression, that is in the class of dispersion models family. The advantages of this approach are illustrated in an experiment studying the mixture effect of fat, carbohydrate, and fiber on tumors’ proportion in mammary glands of rats. Model was evaluated by goodness of fit criteria, simulated envelope charts for residuals of adjusted models, odds ratios graphics and their respective confidence intervals. The simplex regression model showed better quality of fit and smaller odds ratio confidence intervals.

Psychological Capital Questionnaire (PCQ-24): Preliminary Evidence of Psychometric Validity of the Brazilian Version

The PCQ-24 is the main international measure for assessing psychological capital (PsyCap) in organizational contexts. In order to evaluate its adaptation to the Brazilian context, this study aimed to verify preliminary evidences of its psychometric validity. Data collection was conducted online with 749 employees from all regions of Brazil. Confirmatory factor analysis was performed to examine the quality of fit of the second-order factor structure of PCQ-24. The fit indicators were satisfactory (χ2= 742.10, χ2/df= 4.01, p< .001, SRMR= .05, CFI= .91, GFI= .90, TLI= .90, RMSEA= .06). Cronbach’s alpha was .92 and the composite reliability coefficient was .95; in addition, a multigroup confirmatory factorial analysis, comparing male and female participants, demonstrated that the scale is adequate for both groups. These results indicate, in a preliminary way, the validity of PCQ-24 as a measure of psychological capital in the Brazilian labor context.

Regression Modeling and Prediction by Individual Observations versus Frequency

A regression model built by a dataset could sometimes demonstrate a low quality of fit and poor predictions of individual observations. However, using the frequencies of possible combinations of the predictors and the outcome, the same models with the same parameters may yield a high quality of fit and precise predictions for the frequencies of the outcome occurrence. Linear and logistical regressions are used to make an explicit exposition of the results of regression modeling and prediction.