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.

The FUELGEN alternative: an evolutionary approach. The architecture

This paper introduces a few architectural concepts from FUELGEN, that generates a "cloud" of reload patterns, like the generator in the FUELCON expert system, but unlike that generator, is based on a genetic algorithm. There are indications FUELGEN may outperform FUELCON and other tools as reported in the literature, in well-researched case studies, but careful comparisons have to be carried out. This paper complements the information in two other recent papers on FUELGEN. Moreover, a sequel project is outlined.

Exploring genetic alternative concepts for FUELGEN

This paper describes new crossover operators and mutation strategies for the FUELGEN system, a genetic algorithm which designs fuel loading patterns for nuclear power reactors. The new components are applications of new ideas from recent research in genetic algorithms. They are designed to improve the performance of FUELGEN by using information in the problem as yet not made explicit in the genetic algorithm's representation. The paper introduces new developments in genetic algorithm design and explains how they motivate the proposed new components.

A small screen alternative to stone and bronze: The Great War series and British television

This article examines the first major British television series about the First World War, The Great War (BBC, 1964), in terms of its cultural, historical and aesthetic significance. As a central component of the BBC`s 50th anniversary commemorative programme to mark the outbreak of war, the series was a major media event -a small-screen memorial cast in sounds and images instead of stone and bronze. This article looks at how the British television audience responded to this form of on-screen commemoration. Material for this article was derived from the series' extensive production records housed in the BBC Written Archives Centre at Caversham, Berkshire. This was supplemented by, among other sources, material from interviews and correspondence with several surviving members of the production team. This allows a broader understanding of the motivations of those involved in the production of a groundbreaking historical series, while acknowledging the wide-ranging nature of its audience. [From the Publisher]

Drug calculations part 2: alternative strategies to the formula

Drug calculations are an essential skill for nurses. The clinical skill of performing a drug calculation has come under recent scrutiny,resulting in the development of essential skills clusters inwhich pre-registration nurses must be competent before qualifying (Nursing and Midwifery Council 2007). The focuson drug calculation skills places renewed emphasis on how these skills are taught in higher education institutions and how they are learned by students theoretically and in clinical practice.