A simple regional-scale model for forecasting sorghum yield across North-Eastern Australia

Sorghum is the main dryland summer crop in NE Australia and a number of agricultural businesses would benefit from an ability to forecast production likelihood at regional scale. In this study we sought to develop a simple agro-climatic modelling approach for predicting shire (statistical local area) sorghum yield. Actual shire yield data, available for the period 1983-1997 from the Australian Bureau of Statistics, were used to train the model. Shire yield was related to a water stress index (SI) that was derived from the agro-climatic model. The model involved a simple fallow and crop water balance that was driven by climate data available at recording stations within each shire. Parameters defining the soil water holding capacity, maximum number of sowings (MXNS) in any year, planting rainfall requirement, and critical period for stress during the crop cycle were optimised as part of the model fitting procedure. Cross-validated correlations (CVR) ranged from 0.5 to 0.9 at shire scale. When aggregated to regional and national scales, 78-84% of the annual variation in sorghum yield was explained. The model was used to examine trends in sorghum productivity and the approach to using it in an operational forecasting system was outlined. (c) 2005 Elsevier B.V. All rights reserved.

Emotional cue exposure for alcohol abuse: Development of a new treatment procedure to train moderation drinking in the context of dysphoria

Negative mood states are credited to exacerbate excessive drinking among problem drinkers. We developed an emotional cue exposure treatment procedure and applied it to three problem drinkers who have a history of drinking excessively under stressful emotional states. All three preferred a controlled drinking goal and received an average of seven sessions of treatment. Treatment comprised of providing alcohol (priming doses), followed by negative mood induction and response prevention of further drinking. Reductions were observed in the quantity and frequency of drinking, the Beck Depression Inventory, the Severity of Alcohol Dependence Questionnaire (Form C) and the Impaired Control Questionnaire scores. Increments were observed in self-efficacy to face different difficult situations. These gains were maintained at the 6-month follow-up. Providing alcohol to problem drinkers in treatment, followed by negative mood induction and response prevention, is clinically feasible and may benefit clients who drink under a variety of stressful mood states. Copyright (C) 2001 John Wiley & Sons, Ltd.

Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification

The expectation-maximization (EM) algorithm has been of considerable interest in recent years as the basis for various algorithms in application areas of neural networks such as pattern recognition. However, there exists some misconceptions concerning its application to neural networks. In this paper, we clarify these misconceptions and consider how the EM algorithm can be adopted to train multilayer perceptron (MLP) and mixture of experts (ME) networks in applications to multiclass classification. We identify some situations where the application of the EM algorithm to train MLP networks may be of limited value and discuss some ways of handling the difficulties. For ME networks, it is reported in the literature that networks trained by the EM algorithm using iteratively reweighted least squares (IRLS) algorithm in the inner loop of the M-step, often performed poorly in multiclass classification. However, we found that the convergence of the IRLS algorithm is stable and that the log likelihood is monotonic increasing when a learning rate smaller than one is adopted. Also, we propose the use of an expectation-conditional maximization (ECM) algorithm to train ME networks. Its performance is demonstrated to be superior to the IRLS algorithm on some simulated and real data sets.

The case for a new system for oversight of research on human subjects

The increasing emphasis on evidence-based clinical practice has thrown into sharp focus multiple deficiencies in current systems of ethical review. This paper argues that a complete overhaul of systems for ethical oversight of studies involving human subjects is now required as developments in medical, epidemiological and genetic research have outstripped existing structures for ethical supervision. It shows that many problems are now evident and concludes that sequential and piecemeal amendments to present arrangements an inadequate to address these. Ar their core present systems of ethical review still rely on the integrity and judgement of individual investigators. One possible alternative is to train and license research investigators, make explicit their responsibilities and have ethics committees devote much more of their time to monitoring research activity in order to detect those infringing the rules.

Reproducibility of a laboratory-based 40-km cycle time-trial on a stationary wind-trainer in highly trained cyclists

The purpose of the present study was to examine the reproducibility of laboratory-based 40-km cycle time-trial performance on a stationary wind-trainer. Each week, for three consecutive weeks, and on different days, forty-three highly trained male cyclists ((x) over bar +/- SD; age = 25 +/- 6 y; mass = 75 +/- 7 kg; peak oxygen uptake [(V) over dot O-2 peak] = 64.8 +/- 5.2 ml x kg(-1) x min(-1)) performed: 1) a (V) over dot O-2 peak test, and 2) a 40-km time-trial on their own racing bicycle mounted to a stationary wind-trainer (Cateye - Cyclosimulator). Data from all tests were compared using a one-way analysis of variance. Performance on the second and third 40-km time-trials were highly related (r = 0.96; p < 0.001), not significantly different (57:21 +/- 2:57 vs. 57:12 +/- 3:14 min:s), and displayed a low coefficient of variation (CV) = 0.9 +/- 0.7%. Although the first 40-km time-trial (58:43 +/- 3:17min:s) was not significantly different from the second and third tests (p = 0.06), inclusion of the first test in the assessment of reliability increased within-subject CV to 3.0 +/- 2.9%. 40-km time-trial speed (km x h(-1)) was significantly (p < 0.001) related to peak power output (W; r = 0.75), (V) over dot O-2 peak (1 x min(-1); r = 0.53), and the second ventilatory turnpoint (1 x min(-1); r = 0.68) measured during the progressive exercise tests. These data demonstrate that the assessment of 40-km cycle time-trial performance in well-trained endurance cyclists on a stationary wind-trainer is reproducible, provided the athletes perform a familiarization trial.

Using "war stories" to train for adaptive performance: Is it better to learn from error or success?

The study examines whether error exposure training can enhance adaptive performance. Fifty-nine experienced fire-fighters undergoing training for incident command participated in the study. War stories were developed based on real events to illustrate successful and unsuccessful incident command decisions. Two training methodologies were compared and evaluated. One group was trained using case studies that depicted incidents containing errors of management with severe consequences in fire-fighting outcomes (error-story training) while a second group was exposed to the same set of case studies except that the case studies depicted the incidents being managed without errors and their consequences (errorless-story training). The results provide some support for the hypothesis that it is better to learn from other people's errors than from their successes. Implications for training are discussed.

Virtual training environments to improve train driver's crisis decision making

This paper reports on a current research project in which virtual reality simulators are being investigated as a means of simulating hazardous Rail work conditions in order to allow train drivers to practice decision-making under stress. When working under high stress conditions train drivers need to move beyond procedural responses into a response activated through their own problem-solving and decision-making skills. This study focuses on the use of stress inoculation training which aims to build driver’s confidence in the use of new decision-making skills by being repeatedly required to respond to hazardous driving conditions. In particular, the study makes use of a train cab driving simulator to reproduce potentially stress inducing real-world scenarios. Initial pilot research has been undertaken in which drivers have experienced the training simulation and subsequently completed surveys on the level of immersion experienced. Concurrently drivers have also participated in a velocity perception experiment designed to objectively measure the fidelity of the virtual training environment. Baseline data, against which decision-making skills post training will be measured, is being gathered via cognitive task analysis designed to identify primary decision requirements for specific rail events. While considerable efforts have been invested in improving Virtual Reality technology, little is known about how to best use this technology for training personnel to respond to workplace conditions in the Rail Industry. To enable the best use of simulators for training in the Rail context the project aims to identify those factors within virtual reality that support required learning outcomes and use this information to design training simulations that reliably and safely train staff in required workplace accident response skills.