Western Australian primary school teachers knowledge about childhood asthma and its management

A total of 164 primary school teachers from Perth, Western Australia anonymously completed a survey of their knowledge and attitudes about asthma. These teachers were active in assisting children with asthma management but most (91.5%) felt that they did not know enough about asthma. Attitudes toward children with asthma were positive; 97% agreed that such children should be encouraged to participate in sporting activities. Specific knowledge about asthma management and medications was, however, poor. This large sample of Western Australian teachers knew more than their European counterparts but asthma training is needed and should be targeted at improving knowledge of both regular and emergency treatments for asthma.

Effects of memory load and distraction on performance and event-related slow potentials in a visuospatial working memory task

Brain electrical activity related to working memory was recorded at 15 scalp electrodes during a visuospatial delayed response task. Participants (N = 18) touched the remembered position of a target on a computer screen after either a 1 or 8 sec delay. These memory trials were compared to sensory trials in which the target remained present throughout the delay and response periods. Distracter stimuli identical to the target were briefly presented during the delay on 30% of trials. Responses were less accurate in memory than sensory trials, especially after the long delay. During the delay slow potentials developed that were significantly more negative in memory than sensory trials. The difference between memory and sensory trials was greater at anterior than posterior electrodes. On trials with distracters, the slow potentials generated by memory trials showed further enhancement of negativity whereas there were minimal effects on accuracy of performance. The results provide evidence that engagement of visuospatial working memory generates slow wave negativity with a timing and distribution consistent with frontal activation. Enhanced brain activity associated with working memory is required to maintain performance in the presence of distraction. © 1997 by the Massachusetts Institute of Technology

Development and evaluation of neural network freeway incident detection models using field data

This paper discusses a multi-layer feedforward (MLF) neural network incident detection model that was developed and evaluated using field data. In contrast to published neural network incident detection models which relied on simulated or limited field data for model development and testing, the model described in this paper was trained and tested on a real-world data set of 100 incidents. The model uses speed, flow and occupancy data measured at dual stations, averaged across all lanes and only from time interval t. The off-line performance of the model is reported under both incident and non-incident conditions. The incident detection performance of the model is reported based on a validation-test data set of 40 incidents that were independent of the 60 incidents used for training. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. A comparative evaluation between the neural network model and the incident detection model in operation on Melbourne's freeways is also presented. The results of the comparative performance evaluation clearly demonstrate the substantial improvement in incident detection performance obtained by the neural network model. The paper also presents additional results that demonstrate how improvements in model performance can be achieved using variable decision thresholds. Finally, the model's fault-tolerance under conditions of corrupt or missing data is investigated and the impact of loop detector failure/malfunction on the performance of the trained model is evaluated and discussed. The results presented in this paper provide a comprehensive evaluation of the developed model and confirm that neural network models can provide fast and reliable incident detection on freeways. (C) 1997 Elsevier Science Ltd. All rights reserved.

Improving wheat simulation capabilities in Australia from a cropping systems perspective - III. The integrated wheat model (I_WHEAT)

Previous work has identified several short-comings in the ability of four spring wheat and one barley model to simulate crop processes and resource utilization. This can have important implications when such models are used within systems models where final soil water and nitrogen conditions of one crop define the starting conditions of the following crop. In an attempt to overcome these limitations and to reconcile a range of modelling approaches, existing model components that worked demonstrably well were combined with new components for aspects where existing capabilities were inadequate. This resulted in the Integrated Wheat Model (I_WHEAT), which was developed as a module of the cropping systems model APSIM. To increase predictive capability of the model, process detail was reduced, where possible, by replacing groups of processes with conservative, biologically meaningful parameters. I_WHEAT does not contain a soil water or soil nitrogen balance. These are present as other modules of APSIM. In I_WHEAT, yield is simulated using a linear increase in harvest index whereby nitrogen or water limitations can lead to early termination of grainfilling and hence cessation of harvest index increase. Dry matter increase is calculated either from the amount of intercepted radiation and radiation conversion efficiency or from the amount of water transpired and transpiration efficiency, depending on the most limiting resource. Leaf area and tiller formation are calculated from thermal time and a cultivar specific phyllochron interval. Nitrogen limitation first reduces leaf area and then affects radiation conversion efficiency as it becomes more severe. Water or nitrogen limitations result in reduced leaf expansion, accelerated leaf senescence or tiller death. This reduces the radiation load on the crop canopy (i.e. demand for water) and can make nitrogen available for translocation to other organs. Sensitive feedbacks between light interception and dry matter accumulation are avoided by having environmental effects acting directly on leaf area development, rather than via biomass production. This makes the model more stable across environments without losing the interactions between the different external influences. When comparing model output with models tested previously using data from a wide range of agro-climatic conditions, yield and biomass predictions were equal to the best of those models, but improvements could be demonstrated for simulating leaf area dynamics in response to water and nitrogen supply, kernel nitrogen content, and total water and nitrogen use. I_WHEAT does not require calibration for any of the environments tested. Further model improvement should concentrate on improving phenology simulations, a more thorough derivation of coefficients to describe leaf area development and a better quantification of some processes related to nitrogen dynamics. (C) 1998 Elsevier Science B.V.