Artificial neural networks in modelling osmotic dehydration of foods

Artificial neural network (ANN) models for water loss (WL) and solid gain (SG) were evaluated as potential alternative to multiple linear regression (MLR) for osmotic dehydration of apple, banana and potato. The radial basis function (RBF) network with a Gaussian function was used in this study. The RBF employed the orthogonal least square learning method. When predictions of experimental data from MLR and ANN were compared, an agreement was found for ANN models than MLR models for SG than WL. The regression coefficient for determination (R2) for SG in MLR models was 0.31, and for ANN was 0.91. The R2 in MLR for WL was 0.89, whereas ANN was 0.84.Osmotic dehydration experiments found that the amount of WL and SG occurred in the following descending order: Golden Delicious apple > Cox apple > potato > banana. The effect of temperature and concentration of osmotic solution on WL and SG of the plant materials followed a descending order as: 55 > 40 > 32.2C and 70 > 60 > 50 > 40%, respectively.

A conceptual framework for designing pharmaceutical reverse logistics networks in China

Over latest decade, Reverse Logistics (RL) has gained more and more attention from both industry and academia. In the past, most research on RL has been focused on automobile, electronic waste, computer, paper, package and package material. There is very little research and practice on drug recycling. Nevertheless, it is vital important to properly dispose expired drug because of hazardous contain which may harm to people and environment. In China, public awareness of the harmfulness of expired drugs is still very low and very few efforts have been made to recycle drugs. Therefore, this research aims to build up a conceptual framework to indentify factors of influencing drug recycling in China, from scratch borrowing from existing literature and industry practices in other recycling areas. This framework helps in designing reverse logistic (RL) network and also can provide a useful reference tool for policymakers at the local and national level. Furthermore, a primary research is planed to validate the framework and RL network.