University of Southern Queensland

About: University of Southern Queensland is a education organization based out in Toowoomba, Queensland, Australia. It is known for research contribution in the topics: Population & Higher education. The organization has 3037 authors who have published 11241 publications receiving 234781 citations. The organization is also known as: USQ.

Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey

Abstract: The predictive ability of a hybrid model integrating the Firefly Algorithm (FFA), as a heuristic optimization tool with the Multilayer Perceptron (MLP-FFA) algorithm for the prediction of water level in Lake Egirdir, Turkey, is investigated. The accuracy of the hybrid MLP-FFA model is then evaluated against the standalone MLP-based model developed with the Levenberg–Marquadt optimization scheme applied for in the backpropagation-based learning process. To develop and investigate the veracity of the proposed hybrid MLP-FFA model, monthly time scale water level data for 56 years (1961–2016) are applied to train and test the hybrid model. The input combinations of the standalone and the hybrid predictive models are determined in accordance with the Average Mutual Information computed from the historical water level (training) data; generating four statistically significant lagged combinations of historical data to be adopted for the 1-month forecasting of lake water level. The proposed hybrid MLP-FFA model is evaluated with statistical score metrics: Nash–Sutcliffe efficiency, root mean square and mean absolute error, Wilmott’s Index and Taylor diagram developed in the testing phase. The analysis of the results showed that the hybrid MLP–FFA4 model (where 4 months of lagged combinations of lake water level data are utilized) performed more accurately than the standalone MLP4 model. For the fully optimized hybrid (MLP-FFA4) model evaluated in the testing phase, the Willmott’s Index was approximately 0.999 relative to 0.988 (MLP 4) and the root mean square error was approximately 0.029 m and compared to 0.102 m. Moreover, the inter-comparison of the forecasted and the observed data with various other performance metrics (including the Taylor diagram) verified the robustness of the proposed hybrid MLP-FFA4 model over the standalone MLP4 model applied in the problem of forecasting lake water level prediction in the current semi-arid region in Turkey.

Flax fiber and its composites: an overview of water and moisture absorption impact on their performance

Abstract: Contemporary researchers have specified that natural flax fiber is comparable with synthetic fibers due to its unique physical and mechanical characteristics which have been recognized for decades....

Synergistic flame retardancy effect of graphene nanosheets and traditional retardants on epoxy resin

Abstract: Novel non-toxic halogen-free flame retardants are replacing traditional flame retardants in polymer and polymer matrix composite structures. In this study, graphene nanosheet (GNS) is investigatedin combination with traditional layered double hydroxide (LDH), layered rare-earth hydroxide (LRH), and phosphorus-based flame retardant (DOPO) to enhance the flame retardancy of epoxy resin. A synergistic flame retardancy effect is achieved in GNS/LDH and GNS/DOPO systems where combined GNS and LDH increased the viscosity of the epoxy melt, and limited the flame propagation through inhibition of dripping. The limiting oxygen index of epoxy increased from 15.9 to 23.6 with addition of 0.5 wt. % each of GNS and LDH. With the addition of 2.5 wt. % of both GNS and LDH, the total heat release of epoxy resin also reduced from 33.4 MJ/m2 to 24.6 MJ/m2. The synergistic effect of GNS and DOPO adopted a different mechanism. The addition of 2.5 wt. % of GNS and DOPO reduced the peak heat release rate from 1194 kW/m2 to 396 kW/m2, and the total heat release rate from 72.5 MJ/m2 to 48.1 MJ/m2. The synergistic mechanisms of the flame retardants were closely analyzed and correlated with the flame retardant properties.