Search results for: Xiujuan%20Chang

Authors: Xiaodong Xu, Dan Zhao, Xiujuan Chang, Chunming Li, Huiyun Zhou, Xin Li, Qiang Shi, Shifang Luan, Jinghua Yin

Abstract:

Polypropylene (PP) is one of the most commonly used plastics because of its low density, outstanding mechanical properties, and low cost. However, its drawbacks such as low surface energy, poor dyeability, lack of chemical functionalities, and poor compatibility with polar polymers and inorganic materials, have restricted the application of PP. To expand its application in biomedical materials, functionalization is considered to be the most effective way. In this study, PP was functionalized with a chiral monomer, (S)-1-acryloylpyrrolidine-2-carboxylic acid ((S)-APCA), by free-radical grafting in the solid phase. The grafting degree of PP-g-APCA was determined by chemical titration method, and the chemical structure of functionalized PP was characterized by FTIR spectroscopy, which confirmed that the chiral monomer (S)-APCA was successfully grafted onto PP. Static water contact angle results suggested that the surface hydrophilicity of PP was significantly improved by solid phase grafting and assistance of surface water treatment. Protein adsorption and platelet adhesion results showed that hemocompatibility of PP was greatly improved by grafting the chiral monomer.

Keywords: Functionalization, polypropylene, chiral monomer, hemocompatibility.

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Authors: Achela K. Fernando, Xiujuan Zhang, Peter F. Kinley

Abstract:

A feed-forward, back-propagation Artificial Neural Network (ANN) model has been used to forecast the occurrences of wastewater overflows in a combined sewerage reticulation system. This approach was tested to evaluate its applicability as a method alternative to the common practice of developing a complete conceptual, mathematical hydrological-hydraulic model for the sewerage system to enable such forecasts. The ANN approach obviates the need for a-priori understanding and representation of the underlying hydrological hydraulic phenomena in mathematical terms but enables learning the characteristics of a sewer overflow from the historical data. The performance of the standard feed-forward, back-propagation of error algorithm was enhanced by a modified data normalizing technique that enabled the ANN model to extrapolate into the territory that was unseen by the training data. The algorithm and the data normalizing method are presented along with the ANN model output results that indicate a good accuracy in the forecasted sewer overflow rates. However, it was revealed that the accurate forecasting of the overflow rates are heavily dependent on the availability of a real-time flow monitoring at the overflow structure to provide antecedent flow rate data. The ability of the ANN to forecast the overflow rates without the antecedent flow rates (as is the case with traditional conceptual reticulation models) was found to be quite poor.

Keywords: Artificial Neural Networks, Back-propagationlearning, Combined sewer overflows, Forecasting.

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