Prediction of the Influential Factors on Eating Behaviors: A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks.

The importance of eating behavior risk factors in the primary prevention of obesity has been established. Researchers mostly use the linear model to determine associations among these risk factors. However, in reality, the presence of nonlinearity among these factors causes a bias in the prediction models. The aim of this study was to explore the potential of a hybrid model to predict the eating behaviors. The hybrid model of structural equation modelling (SEM) and artificial neural networks (ANN) was applied to evaluate the prediction model. The SEM analysis was used to check the relationship of the emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) and their effect on different categories of eating behavior patterns (EBP). In the second step, the input and output required for ANN analysis were obtained from SEM analysis and were applied in the neural network model. 340 university students participated in this study. The hybrid model (SEM-ANN) was conducted using multilayer perceptron (MLP) with feed-forward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The tangent/sigmoid function was used for the input layer, while the linear function was applied for the output layer. The coefficient of determination (R 2) and mean square error (MSE) were calculated. Using the hybrid model, the optimal network happened at MLP 3-17-8. It was proved that the hybrid model was superior to SEM methods because the R 2 of the model was increased by 27%, while the MSE was decreased by 9.6%. Moreover, it was found that BSC, BAS, and EES significantly affected healthy and unhealthy eating behavior patterns. Thus, a hybrid approach could be suggested as a significant methodological contribution from a machine learning standpoint, and it can be implemented as software to predict models with the highest accuracy.

A comprehensive review on nanocatalysts and nanobiocatalysts for biodiesel production in Indonesia, Malaysia, Brazil and USA.

Biodiesel is an alternative to fossil-derived diesel with similar properties and several environmental benefits. Biodiesel production using conventional catalysts such as homogeneous, heterogeneous, or enzymatic catalysts faces a problem regarding catalysts deactivation after repeated reaction cycles. Heterogeneous nanocatalysts and nanobiocatalysts (enzymes) have shown better advantages due to higher activity, recyclability, larger surface area, and improved active sites. Despite a large number of studies on this subject, there are still challenges regarding its stability, recyclability, and scale-up processes for biodiesel production. Therefore, the purpose of this study is to review current modifications and role of nanocatalysts and nanobiocatalysts and also to observe effect of various parameters on biodiesel production. Nanocatalysts and nanobiocatalysts demonstrate long-term stability due to strong Brønsted-Lewis acidity, larger active spots and better accessibility leading to enhancethe biodiesel production. Incorporation of metal supporting positively contributes to shorten the reaction time and enhance the longer reusability. Furthermore, proper operating parameters play a vital role to optimize the biodiesel productivity in the commercial scale process due to higher conversion, yield and selectivity with the lower process cost. This article also analyses the relationship between different types of feedstocks towards the quality and quantity of biodiesel production. Crude palm oil is convinced as the most prospective and promising feedstock due to massive production, low cost, and easily available. It also evaluates key factors and technologies for biodiesel production in Indonesia, Malaysia, Brazil, and the USA as the biggest biodiesel production supply.