Coursework uploads and zero-tolerance deadlines

Clear assessment deadlines and severe penalties for late submission of coursework are a feature of a number of UK universities. This presents a severe challenge for any online upload system. Evidence from a range of different implementations at the School of Computing and Mathematical Sciences at the University of Greenwich over the past few years is examined to assess the impact of a zero-tolerance deadline policy on the way students work and the problems that arise. Suggestions are made on how to minimise any possible negative impact of a zero-tolerance deadline policy on the administration of the system and on staff and students.

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.