Detailed study on fusion characteristics of rigid poly(vinyl chloride) nanocomposites: The comparison of using multiple regression analysis and artificial neural network

Fusion behavior of poly(vinyl chloride) (PVC) compounds plays an important role in the development of physical properties of processed material. The fusion characteristics in PVC processing are governed by material variables that affect the fusion with some interactions. In this research, the aim was to characterize the effects of formulation ingredients on fusion characteristics of PVC. Four material parameters, including the contents of nanoclay (NC), azodicarbonamide, calcium stearate, and processing aid, are proposed as affecting variables. The fusion time (FT) as well as fusion factor (FF) are considered fusion indicators and are experimentally determined in some different levels of affecting parameters. The multivariable regression analysis (MRA) and the Artificial Neural Network (ANN) modeling are considered as two analytical methods. The regression analysis result for the FT denotes, in part, significant linear and quadratic effects of NC and also its significant interactions with azodicarbonamide and calcium stearate, whereas that of FF indicates only a linear effect of NC. ANN modeling is performed with a three‐layer (input, hidden, and output) neural network. The results of the comparison of the MRA and ANN predictions with experimental values are reported as the correlation coefficient (R²), mean‐square error, and mean absolute percentage error for both FF and FT parameters. The obtained values clearly denote that the ANN results are more precise and especially more general than those of MRA. However, in the case of FT, improvement of the ANN modeling is much greater than that of FF. J. VINYL ADDIT. TECHNOL., 21:147–155, 2015. © 2014 Society of Plastics Engineers     

Effects of urban coarse particles inhalation on oxidative and inflammatory parameters in the mouse lung and colon

Background

Air pollution is a recognized aggravating factor for pulmonary diseases and has notably deleterious effects on asthma, bronchitis and pneumonia. Recent studies suggest that air pollution may also cause adverse effects in the gastrointestinal tract. Accumulating experimental evidence shows that immune responses in the pulmonary and intestinal mucosae are closely interrelated, and that gut-lung crosstalk controls pathophysiological processes such as responses to cigarette smoke and influenza virus infection. Our first aim was to collect urban coarse particulate matter (PM) and to characterize them for elemental content, gastric bioaccessibility, and oxidative potential; our second aim was to determine the short-term effects of urban coarse PM inhalation on pulmonary and colonic mucosae in mice, and to test the hypothesis that the well-known antioxidant N-acetyl-L-cysteine (NAC) reverses the effects of PM inhalation.

Results

The collected PM had classical features of urban particles and possessed oxidative potential partly attributable to their metal fraction. Bioaccessibility study confirmed the high solubility of some metals at the gastric level. Male mice were exposed to urban coarse PM in a ventilated inhalation chamber for 15 days at a concentration relevant to episodic elevation peak of air pollution. Coarse PM inhalation induced systemic oxidative stress, recruited immune cells to the lung, and increased cytokine levels in the lung and colon. Concomitant oral administration of NAC reversed all the observed effects relative to the inhalation of coarse PM.

Conclusions

Coarse PM-induced low-grade inflammation in the lung and colon is mediated by oxidative stress and deserves more investigation as potentiating factor for inflammatory diseases.

     

A robust optimization approach for imprecise data envelopment analysis

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the input and output data in real-world problems are often imprecise or ambiguous. Some researchers have proposed interval DEA (IDEA) and fuzzy DEA (FDEA) to deal with imprecise and ambiguous data in DEA. Nevertheless, many real-life problems use linguistic data that cannot be used as interval data and a large number of input variables in fuzzy logic could result in a significant number of rules that are needed to specify a dynamic model. In this paper, we propose an adaptation of the standard DEA under conditions of uncertainty. The proposed approach is based on a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set. Our robust DEA (RDEA) model seeks to maximize efficiency (similar to standard DEA) but under the assumption of a worst case efficiency defied by the uncertainty set and it’s supporting constraint. A Monte-Carlo simulation is used to compute the conformity of the rankings in the RDEA model. The contribution of this paper is fourfold: (1) we consider ambiguous, uncertain and imprecise input and output data in DEA; (2) we address the gap in the imprecise DEA literature for problems not suitable or difficult to model with interval or fuzzy representations; (3) we propose a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set; and (4) we use Monte-Carlo simulation to specify a range of Gamma in which the rankings of the DMUs occur with high probability.     

An extended compromise ratio method for fuzzy group multi-attribute decision making with SWOT analysis

The technique for order preference by similarity to ideal solution (TOPSIS) is a well-known multi-attribute decision making (MADM) method that is used to identify the most attractive alternative solution among a finite set of alternatives based on the simultaneous minimization of the distance from an ideal solution (IS) and the maximization of the distance from the nadir solution (NS). We propose an alternative compromise ratio method (CRM) using an efficient and powerful distance measure for solving the group MADM problems. In the proposed CRM, similar to TOPSIS, the chosen alternative should be simultaneously as close as possible to the IS and as far away as possible from the NS. The conventional MADM problems require well-defined and precise data; however, the values associated with the parameters in the real-world are often imprecise, vague, uncertain or incomplete. Fuzzy sets provide a powerful tool for dealing with the ambiguous data. We capture the decision makers’ (DMs’) judgments with linguistic variables and represent their importance weights with fuzzy sets. The fuzzy group MADM (FGMADM) method proposed in this study improves the usability of the CRM. We integrate the FGMADM method into a strengths, weaknesses, opportunities and threats (SWOT) analysis framework to show the applicability of the proposed method in a solar panel manufacturing firm in Canada.     

Experimental and macro finite element modeling studies on conformability behavior of woven nylon 66 composite reinforcement

Abstract

In this study, 3D hemispherical forming experiments are conducted to analyze the conformability behavior of nylon 66 plain woven reinforcements with different weft densities. To make insight into the forming process, a macro finite element model is also proposed for 3D forming of a specific woven reinforcement by using the defined ‘Fabric’ material. The results show that the proposed FEM modelling is highly correlated with experimental findings in terms of forming energy values with small and insignificant errors values, which confirm well model validity. It shows that the nylon 66 composite woven reinforcement with a lower weft density exhibits a lower forming energy (toughness) and hence a higher conformability over a hemispherical surface. On the other hand, modeling outputs clearly indicate that more wrinkling intensity appear for woven reinforcement with a higher weft density.