Evaluation of the sustainability of water withdrawals in the United States, 1995 to 2025

To evaluate the long term sustainability of water withdrawals in the United States, a county level analysis of the availability of renewable water resources was conducted, and the magnitudes of human withdrawals from surface water and ground water sources and the stored water requirements during the warmest months of the year were evaluated. Estimates of growth in population and electricity generation were then used to estimate the change in withdrawals assuming that the rates of water use either remain at their current levels (the business as usual scenario) or that they exhibit improvements in efficiency at the same rate as observed over 1975 to 1995 (the improved efficiency scenario). The estimates show several areas, notably the Southwest and major metropolitan areas throughout the United States, as being likely to have significant new storage requirements with the business-as-usual scenario, under the condition of average water availability. These new requirements could be substantially eliminated under the improved efficiency scenario, thus indicating the importance of water use efficiency in meeting future requirements. The national assessment identified regions of potential water sustainability concern; these regions can be the subject of more targeted data collection and analyses in the future.

Modelling the behaviour of the new issue market

This paper analyses the time series behaviour of the initial public offering (IPO) market using an equilibrium model of demand and supply that incorporates the number of new issues, average underpricing, and general market conditions. Model predictions include the existence of serial correlation in both the number of new issues and the average level of underpricing, as well as interactions between these variables and the impact of general market conditions. The model is tested using 40 years of monthly IPO data. The empirical results are generally consistent with predictions.

Mefloquine and doxycycline malaria prophylaxis in Australian soldiers in East Timor

Objectives: To describe the tolerability of mefloquine in Australian soldiers for malaria prophylaxis, including a comparison with doxycycline. Design: Open-label, prospective study and cross-sectional questionnaire and interview. Setting and participants: Two contingents of Australian soldiers, each deployed to East Timor for peacekeeping duties over a 6-month period (April 2001-October 2001 and October 2001-May 2002). Outcome measures: Withdrawals during the study; adverse events relating to mefloquine prophylaxis; willingness to use mefloquine again on deployment. Results: Of 1157 soldiers starting on mefloquine, 75 (6.5%) withdrew because of adverse responses to the drug. There were three serious adverse events of a neuropsychiatric nature, possibly relating to mefloquine. Fifty-seven per cent of soldiers using mefloquine prophylaxis reported at least one adverse event, compared with 56% using doxycycline. The most commonly reported adverse effects of both drugs were sleep disturbance, headache, tiredness and nausea. Of the 968 soldiers still taking mefloquine at the end of their deployments, 94% indicated they would use mefloquine again. Of 388 soldiers taking doxycycline prophylaxis who were deployed with the first mefloquine study contingent, 89% indicated they would use doxycycline again. Conclusions: Mefloquine was generally well tolerated by Australian soldiers and should continue to be used for those intolerant of doxycycline.

Rule extraction from technology IPOs in the US stock market

Machine learning techniques for prediction and rule extraction from artificial neural network methods are used. The hypothesis that market sentiment and IPO specific attributes are equally responsible for first-day IPO returns in the US stock market is tested. Machine learning methods used are Bayesian classifications, support vector machines, decision tree techniques, rule learners and artificial neural networks. The outcomes of the research are predictions and rules associated With first-day returns of technology IPOs. The hypothesis that first-day returns of technology IPOs are equally determined by IPO specific and market sentiment is rejected. Instead lower yielding IPOs are determined by IPO specific and market sentiment attributes, while higher yielding IPOs are largely dependent on IPO specific attributes.