The Drop Technique: a Method to Control the Amount of Fluoride Dentifrice Used by Young Children

Purpose: The purpose of this study was to evaluate the amount of dentifrice applied to the toothbrush by school children using a liquid dentifrice (drop technique), when compared to toothpaste. Materials and Methods: A total of 178 school children (4-8 years old) from two cities in Brazil (Bauru and Bariri) participated in the present two-part crossover study. Children from Bauru received training regarding tooth-brushing techniques and use of dentifrice before data collection. In each phase, the amount of toothpaste or liquid dentifrice applied by the children to the toothbrush was measured, using a portable analytical balance (+/- 0.01 g). Data were tested by analysis of covariance (Ancova) and linear regression (p < 0.05). Results: The mean (+/- standard deviation) amounts of toothpaste and liquid dentifrice applied to the toothbrushes for children from Bauru were 0.41 +/- 0.20 g and 0.15 +/- 0.06 g, respectively. For children from Bariri, the amounts applied were and 0.48 +/- 0.24 g and 0.14 +/- 0.05 g, respectively. The amount of toothpaste applied was significantly larger than the amount of liquid dentifrice for both cities. Children from Bariri applied a significantly larger amount of toothpaste, when compared to those from Bauru. However, for the liquid dentifrice, there was no statistically significant difference between the cities. A significant correlation between the amount of toothpaste applied and the age of the children was verified, but the same was not found for the liquid dentifrice. Conclusion: The use of the drop technique reduced and standardised the amount of dentifrice applied to the toothbrush, which could reduce the risk of dental fluorosis for young children.

The composite Euler method for stiff stochastic differential equations

In this paper we present the composite Euler method for the strong solution of stochastic differential equations driven by d-dimensional Wiener processes. This method is a combination of the semi-implicit Euler method and the implicit Euler method. At each step either the semi-implicit Euler method or the implicit Euler method is used in order to obtain better stability properties. We give criteria for selecting the semi-implicit Euler method or the implicit Euler method. For the linear test equation, the convergence properties of the composite Euler method depend on the criteria for selecting the methods. Numerical results suggest that the convergence properties of the composite Euler method applied to nonlinear SDEs is the same as those applied to linear equations. The stability properties of the composite Euler method are shown to be far superior to those of the Euler methods, and numerical results show that the composite Euler method is a very promising method. (C) 2001 Elsevier Science B.V. All rights reserved.

Considerations in the design of a mixed-method cluster evaluation of a community programme for 'at-risk' young people

This article discusses the design of a comprehensive evaluation of a community development programme for young people 'at-risk' of self-harming behaviour. It outlines considerations in the design of the evaluation and focuses on the complexities and difficulties associated with the evaluation of a community development programme. The challenge was to fulfil the needs of the funding body for a broad, outcome-focused evaluation while remaining close enough to the programme to accurately represent its activities and potential effects at a community level. Specifically, the strengths and limitations of a mixed-method evaluation plan are discussed with recommendations for future evaluation practice.

Memory awareness and schematization: Learning in the university context

Following the application of the remember/know paradigm to student learning by Conway et al. (1997), this study examined changes in learning and memory awareness of university students in a lecture course and a research methods course. The proposed shift from a dominance of 'remember' awareness in early learning to a dominance of 'know' awareness as learning progresses and schematization occurs was evident for the methods course but not for the lecture course. The patterns of remember and know awareness and proposed associated levels of schematization were supported by a separate measure of the quality of student learning using the SOLO (Structure of Observed Learning Outcomes) Taxonomy. As found by previous research, the remember-to-know shift and schematization of knowledge is dependent upon type of course and level of achievement. Findings are discussed in terms of the utility of the methodology used, the theoretical implications and the applications to educational practice. Copyright (C) 2001 John Wiley & Sons, Ltd.

Learning organizations: Empirically investigating metaphors

Learning organizations are a special form of organization where enhancing learning is a strategy to increase intellectual capital. Developing learning organizations has become an imperative for many managers, since an organization's learning methods and rate may be the only source of sustainable competitive advantage. However, learning organization theory tends to be prescriptive and rhetorical, with empirical research still relatively new. This paper contributes to the literature by reporting case-study research in progress based on four Australian organizations. In the organizations studied, use of the learning organization metaphor was coupled with an emergent metaphor: organization as `family". By employing structure mapping of metaphor within analytical induction, both established methods but not combined before, this paper shows how theory might be developed from metaphor.

A new wavelet-based method for the solution of the population balance equation

A new wavelet-based method for solving population balance equations with simultaneous nucleation, growth and agglomeration is proposed, which uses wavelets to express the functions. The technique is very general, powerful and overcomes the crucial problems of numerical diffusion and stability that often characterize previous techniques in this area. It is also applicable to an arbitrary grid to control resolution and computational efficiency. The proposed technique has been tested for pure agglomeration, simultaneous nucleation and growth, and simultaneous growth and agglomeration. In all cases, the predicted and analytical particle size distributions are in excellent agreement. The presence of moving sharp fronts can be addressed without the prior investigation of the characteristics of the processes. (C) 2001 Published by Elsevier Science Ltd.

An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.