A simple data logger for student-designed rocket experiments.

The final-year project for Mechanical & Space Engineering students at UQ often involves the design and flight testing of an experiment. This report describes the design and use of a simple data logger that should be suitable for collecting data from the students' flight experiments. The exercise here was taken as far as the construction of a prototype device that is suitable for ground-based testing, say, the static firing of a hybrid rocket motor.

Comparison of surveys used to measure physical activity

Objective: To compare the level of agreement in results obtained from four physical activity (PA) measurement instruments that are in use in Australia and around the world. Methods: 1,280 randomly selected participants answered two sets of PA questions by telephone. 428 answered the Active Australia (AA) and National Health Surveys, 427 answered the AA and CDC Behavioural Risk Factor Surveillance System surveys (BRFSS), and 425 answered the AA survey and the short International Physical Activity Questionnaire (IPAQ). Results: Among the three pairs of survey items, the difference in mean total PA time was lowest when the AA and NHS items were asked (difference=24) (SE:17) minutes, compared with 144 (SE:21) mins for AA/BRFSS and 406 (SE:27) mins for AA/IPAQ). Correspondingly, prevalence estimates for 'sufficiently active' were similar for AA and NHS (56% and 55% respectively), but about 10% higher when BRFSS data were used, and about 26% higher when the IPAQ items were used, compared with estimates from the AA survey. Conclusions: The findings clearly demonstrate that there are large differences in reported PA times and hence in prevalence estimates of 'sufficient activity' from these four measures. Implications: It is important to consistently use the same survey for population monitoring purposes. As the AA survey has now been used three times in national surveys, its continued use for population surveys is recommended so that trend data ever a longer period of time can be established.

Equational Reasoning as a Tool for Data Analysis

A combination of deductive reasoning, clustering, and inductive learning is given as an example of a hybrid system for exploratory data analysis. Visualization is replaced by a dialogue with the data.

Leadership Attributes and Cultural Values in Australia and New Zealand Compared: An Initial Report Based on GLOBE Data.

This paper reports a comparative study of Australian and New Zealand leadership attributes, based on the GLOBE (Global Leadership and Organizational Behavior Effectiveness) program. Responses from 344 Australian managers and 184 New Zealand managers in three industries were analyzed using exploratory and confirmatory factor analysis. Results supported some of the etic leadership dimensions identified in the GLOBE study, but also found some emic dimensions of leadership for each country. An interesting finding of the study was that the New Zealand data fitted the Australian model, but not vice versa, suggesting asymmetric perceptions of leadership in the two countries.

Selection bias in gene extraction on the basis of microarray gene-expression data

In the context of cancer diagnosis and treatment, we consider the problem of constructing an accurate prediction rule on the basis of a relatively small number of tumor tissue samples of known type containing the expression data on very many (possibly thousands) genes. Recently, results have been presented in the literature suggesting that it is possible to construct a prediction rule from only a few genes such that it has a negligible prediction error rate. However, in these results the test error or the leave-one-out cross-validated error is calculated without allowance for the selection bias. There is no allowance because the rule is either tested on tissue samples that were used in the first instance to select the genes being used in the rule or because the cross-validation of the rule is not external to the selection process; that is, gene selection is not performed in training the rule at each stage of the cross-validation process. We describe how in practice the selection bias can be assessed and corrected for by either performing a cross-validation or applying the bootstrap external to the selection process. We recommend using 10-fold rather than leave-one-out cross-validation, and concerning the bootstrap, we suggest using the so-called. 632+ bootstrap error estimate designed to handle overfitted prediction rules. Using two published data sets, we demonstrate that when correction is made for the selection bias, the cross-validated error is no longer zero for a subset of only a few genes.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

Using household survey data to inform policy decisions regarding the delivery of evidence-based parenting interventions.

Background: This study used household survey data on the prevalence of child, parent and family variables to establish potential targets for a population-level intervention to strengthen parenting skills in the community. The goals of the intervention include decreasing child conduct problems, increasing parental self-efficacy, use of positive parenting strategies, decreasing coercive parenting and increasing help-seeking, social support and participation in positive parenting programmes. Methods: A total of 4010 parents with a child under the age of 12 years completed a statewide telephone survey on parenting. Results: One in three parents reported that their child had a behavioural or emotional problem in the previous 6 months. Furthermore, 9% of children aged 2–12 years meet criteria for oppositional defiant disorder. Parents who reported their child's behaviour to be difficult were more likely to perceive parenting as a negative experience (i.e. demanding, stressful and depressing). Parents with greatest difficulties were mothers without partners and who had low levels of confidence in their parenting roles. About 20% of parents reported being stressed and 5% reported being depressed in the 2 weeks prior to the survey. Parents with personal adjustment problems had lower levels of parenting confidence and their child was more difficult to manage. Only one in four parents had participated in a parent education programme. Conclusions: Implications for the setting of population-level goals and targets for strengthening parenting skills are discussed.

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.

The kinetics of NO and N2O reduction over coal chars in fluidised-bed combustion

This paper presents a comprehensive and critical review of the mechanisms and kinetics of NO and N2O reduction reaction with coal chars under fluidised-bed combustion conditions (FBC). The heterogeneous reactions of NO and N2O with char/carbon surface have been well recognised as the most important processes in reducing both NOx and N2O in situ FBC. Compared to NO-carbon reactions in FBC, the reactions of N2O with chars have been relatively less understood and studied. Beginning with the overall reaction schemes for both NO and N2O reduction, the paper extensively discusses the reaction mechanisms including the effects of active surface sites. Generally, NO- and N2O-carbon reactions follow a series of step reactions. However, questions remain concerning the role of adsorbed phases of NO and N2O, and the behaviour of different surface sites. Important kinetics factors such as the rate expressions, kinetics parameters as well as the effects of surface area and pore structure are discussed in detail. The main factors influencing the reduction of NO and N2O in FBC conditions are the chemical and physical properties of chars, and the operating parameters of FBC such as temperature, presence of CO, O-2 and pressure. It is shown that under similar conditions, N2O is more readily reduced on the char surface than NO. Temperature was found to be a very important parameter in both NO and N2O reduction. It is generally agreed that both NO- and N2O-carbon reactions follow first-order reaction kinetics with respect to the NO and N2O concentrations. The kinetic parameters for NO and N2O reduction largely depend on the pore structure of chars. The correlation between the char surface area and the reactivities of NO/N2O-char reactions is considered to be of great importance to the determination of the reaction kinetics. The rate of NO reduction by chars is strongly enhanced by the presence of CO and O-2, but these species may not have significant effects on the rate of N2O reduction. However, the presence of these gases in FBC presents difficulties in the study of kinetics since CO cannot be easily eliminated from the carbon surface. In N2O reduction reactions, ash in chars is found to have significant catalytic effects, which must be accounted for in the kinetic models and data evaluation. (C) 1997 Elsevier Science Ltd.

Development of a dietary supplement database

Data describing the composition of dietary supplements are not readily available to the public health community. As a result, intake from dietary supplements is generally not considered in most dietary surveys and, hence, little is known about the significance of supplement intake in relation to total diet or disease risk. To enable a more comprehensive analysis of dietary data, a database of the composition of various dietary supplements has been compiled. Active ingredients of all dietary supplements sold in Australia are included in the Australian Register of Therapeutic Goods (ARTG), maintained by the Therapeutic Goods Administration. Products included in the database were restricted to those vitamin, mineral and other supplements identified in dietary data collected from studies conducted in southeast Queensland and New South Wales (850 supplements). Conversion factors from ingredients compounds to active elements were compiled from standard sources. No account has been made for bioavailability, consistent with current practice for food composition databases. The database can be queried by ARTG identification number, brand, product title, or a variety of other fields. Expected future developments include development of standard formulations for use when supplements are incompletely specified, and expansion of products included for more widespread use.

Evaluation of multifrequency bioelectrical impedance data for predicting lean tissue mass in beef cattle

Multifrequency bioimpedance analysis has the potential to provide a non-invasive technique for determining body composition in live cattle. A bioimpedance meter developed for use in clinical medicine was adapted and evaluated in 2 experiments using a total of 31 cattle. Prediction equations were obtained for total body water, extracellular body water, intracellular body water, carcass water and carcass protein. There were strong correlations between the results obtained through chemical markers and bioimpedance analysis when determined in cattle that had a wide range of liveweights and conditions. The r(2) values obtained were 0.87 and 0.91 for total body water and extracellular body water respectively. Bioimpedance also correlated with carcass water, measured by chemical analysis (r(2) = 0.72), but less well with carcass protein (r(2) = 0.46). These correlations were improved by inclusion of liveweight and sex as variables in multiple regression analysis. However, the resultant equations were poor predictors of protein and water content in the carcasses of a group of small underfed beef cattle, that had a narrow range of liveweights. In this case, although there was no statistical difference between the predicted and measured values overall, bioimpedance analysis did not detect the differences in carcass protein between the 2 groups that were apparent following chemical analysis. Further work is required to determine the sensitivity of the technique in small underfed cattle, and its potential use in heavier well fed cattle close to slaughter weight.