Identification of the causes of subsoil ammonium accumulations in southeastern Queensland

Unusually high concentrations of exchangeable-NH4+ (up to 270 kg-N/ha) were observed in a Vertisol below 1 m in southeast Queensland. This study aimed to identify the source of this NH4+. Preliminary sampling of native vegetation and cropping areas had found that elevated NH4+was only present under cropped soil, indicating that clearing was linked to the NH4+formation. Mechanisms of NH4+formation that may have occurred in the subsoil after clearing were hypothesised to be a) mineralisation of organic-N; b) NO3- reduction to NH4+; and/or c) the release of fixed-NH4+. In addition it was proposed that nitrification was inhibited in the subsoil, and that this allowed any NH4+formed to accumulate over time. Incubation experiments to examine nitrification rates revealed that nitrification was undetectable, and appeared to be limited by a combination of subsoil acidity and low numbers of nitrifying organisms. Mineralisation studies also revealed that the mineralisation of organic-N was undetectable, and that mineralising organisms were limited by acidity. A small amount of nitrate ammonification could be observed with the aid of a 15N tracer if the soil was waterlogged. However, this NH4+was insufficient to account for the overall NH4+accumulation, and these waterlogged conditions were not observed in the field. Concentrations of fixed- NH4+ measured were also too low to have been responsible for the accumulation of exchangeable-NH4+. It was concluded that none of the proposed hypotheses of NH4+formation could account for the NH4+accumulation observed.

Biologically inspired joint control for a humanoid robot

The GuRm is a 1.2m tall, 23 degree of freedom humanoid consuucted at the University of Queensland for research into humanoid robotics. The key challenge being addressed by the GuRw projcct is the development of appropriate learning strategies for control and coodinadon of the robot’s many joints. The development of learning strategies is Seen as a way to sidestep the inherent intricacy of modeling a multi-DOP biped robot. This paper outlines the approach taken to generate an appmpria*e control scheme for the joinis of the GuRoo. The paper demonsrrates the determination of local feedback control parameters using a genetic algorithm. The feedback loop is then augmented by a predictive modulator that learns a form of feed-fonward control to overcome the irregular loads experienced at each joint during the gait cycle. The predictive modulator is based on thc CMAC architecture. Results from tats on the GuRoo platform show that both systems provide improvements in stability and tracking of joint control.