The effects of the late 2000s global financial crisis on Australia's construction demand

An accurate measurement of the impacts of external shocks on construction demand will enable construction industry policymakers and developers to make allowances for future occurrences and advance the construction industry in a sustainable manner. This paper aims to measurethe dynamic effects of the late 2000s global financial crisis on the level of demand in the Australian construction industry. The vector error correction (VEC) model with intervention indicators is employed to estimate the external impact from the crisis on a macro-level construction economic indicator, namely construction demand. The methodology comprises six main stages to produce appropriate VEC models that describe the characteristics of the underlying process. Research findings suggestthat overall residential and non-residential construction demand were affected significantly by the recent crisis and seasonality. Non-residentialconstruction demand was disrupted more than residential construction demand at the crisis onset. The residential constructionindustry is more reactive and is able to recover faster following the crisis in comparison with the non-residential industry. The VEC model with intervention indicators developed in this study can be used as an experiment for an advanced econometric method. This can be used to analyse the effects of special eventsand factors not only on construction but also on other industries.

The functional significance of ventilation frequency, and its relationship to oxygen demand in the resting brown long-eared bat, Plecotus auritus

Mean oxygen consumption and simultaneous ventilation frequency of nine non-reproductive brown long-eared bats (body mass 8.53–13.33 g) were measured on 159 occasions. Ambient (chamber) temperature at which the measurements were made ranged from 10.8 to 41.1°C. Apneic ventilation occurred in 22 of the 59 measurements made when mean oxygen consumption was less than 0.5 ml·min-1. No records of apneic ventilation were obtained when it was over 0.5 ml·min-1. The relationship between ventilation frequency and mean oxygen consumption depended on whether ventilation was apneic or non-apneic. When ventilation was non-apneic the relationship was positive and log-linear. When ventilation was apneic the relationship was log-log. Within the thermoneutral zone ventilation frequency was not significantly different from that predicted from allometric equations for a terrestrial mammal of equivalent body mass, but was significantly greater than that predicted for a bird. A reduction in the amount of oxygen consumed per breath occurred at ambient temperatures above the upper critical temperature (39°C).

Bayesian model averaging of load demand forecasts from neural network models

Creating a set of a number of neural network (NN) models in an ensemble and accumulating them can achieve better overview capability as compared to single neural network. Neural network ensembles are designed to provide solutions to particular problems. Many researchers and academicians have adopted this NN ensemble technique, especially in machine learning, and has been applied in various fields of engineering, medicine and information technology. This paper present a robust aggregation methodology for load demand forecasting based on Bayesian Model Averaging of a set of neural network models in an ensemble. This paper estimate a vector of coefficient for individual NN models' forecasts using validation data-set. These coefficients, also known as weights, are equal to posterior probabilities of the models generating the forecasts. These BMA weights are then used in combining forecasts generated from NN models with test data-set. By comparing the Bayesian results with the Simple Averaging method, it was observed that benefits are obtained by utilizing an advanced method like BMA for forecast combinations.