A computer program, ARAGORN, identifies tRNA and tmRNA genes. The program employs heuristic algorithms to predict tRNA secondary structure, based on homology with recognized tRNA consensus sequences and ability to form a base-paired cloverleaf. tmRNA genes are identified using a modified version of the BRUCE program. ARAGORN achieves a detection sensitivity of 99% from a set of 1290 eubacterial, eukaryotic and archaeal tRNA genes and detects all complete tmRNA sequences in the tmRNA database, improving on the performance of the BRUCE program. Recently discovered tmRNA genes in the chloroplasts of two species from the 'green' algae lineage are detected. The output of the program reports the proposed tRNA secondary structure and, for tmRNA genes, the secondary structure of the tRNA domain, the tmRNA gene sequence, the tag peptide and a list of organisms with matching tmRNA peptide tags.

An online version, software for download and test results are available at www.acgt.se/online.html

A computer program, BRUCE, was developed for the identification of transfer-messenger RNA (tmRNA) genes. The program employs heuristic algorithms to search for a tRNA(Ala)-like secondary structure surrounding a short sequence encoding the tag peptide. In the 57 completely sequenced bacterial genomes where tmRNA genes have been reported previously, BRUCE identified all with no false positives. In addition, BRUCE found 99 of the 100 tmRNAs identified previously in other bacteria, red chloroplasts and cyanelles. The output of the program reports the proposed tRNA secondary structure, the tmRNA gene sequence and the tag peptide.

An interactive web tool was created to simulate 100% renewable electricity supply scenarios for the SouthWest Interconnected System (SWIS) in the southwest of Western Australia. The SWIS is isolated from other grids and currently has no available hydropower. Hence it makes a good case study of how supply and demand might be balanced on an hour-by-hour basis and grid stability maintained without the benefit of energy import/export or pumped hydroelectric storage. The tool included regional models for wind and solar power, so that hypothetical power stations were not confined to sites with existing wind farms or solar power stations, or sites with measurements of wind speed and solar radiation. A generic model for solar thermal storage and simple models for energy efficiency, distributed battery storage and power to gas storage were also developed. Due to the urgency of climate change mitigation a rapid construction schedule of completion by 2030, rather than the more common target of 2050, was set. A scenario with high wind generation, and scenarios with varying levels of solar power, wind power, distributed battery storage, energy efficiency improvements and power to gas systems were considered. The battery storage system and PV arrays were configured to provide synthetic inertia to maintain grid stability (with a small loss in capacity for each), and existing synchronous generators were kept spinning with no fuel input, adding a small increase to the electrical load demand. The level of synthetic inertia provided by battery storage was estimated for each scenario. The results indicated that a balanced mix of solar PV, solar thermal, efficiency, and storage were the most feasible to be built on a rapid time scale. The required capacity and build rate of the generation and storage systems would be reduced if energy efficiency improvements were implemented on a more rapid schedule compared to the current global improvement rate. The scenario with very high

A simple simulator capable of generating synthetic hourly values of wind power was developed for the South West region of Western Australia. The global Modern Era Retrospective Analysis for Research and Applications (MERRA) atmospheric database was used to calibrate the simulation with wind speeds 50m above ground level. Analysis of the MERRA data indicated that the normalised residual of hourly wind speed had a double exponential distribution. A translated square-root transformation function y n =( √ (1.96+ y e )−1.4)/0.302 was used to convert this to a normal-like distribution so that autoregressive (AR) time series analysis could be used. There was a significant dependency in this time series on the last three hours, so a third order AR model was used to generate hourly 50m wind speed residuals. The MERRA daily average 50m wind speed was found to have a Weibull-like distribution, so a square root conversion was used on the data to obtain a normal distribution. The time series for this distribution was found to have a significant dependency on the values for the last two days, so a second order AR model was also used in the simulation to generate synthetic time series values for the square root of the daily average wind speed. Seasonal, daily, diurnal, and hourly components were added to generate synthetic time series values of total 50m wind speed. To scale this wind speed to turbine hub height, a time varying wind shear factor model was created and calibrated using measured data at a coastal and an inland site. Standard wind turbine power curves were modified to produce an estimate of wind farm power output from the hub-height wind speed. Comparison with measured grid supervisory control and data acquisition (SCADA) data indicated that the simulation generated conservative power output values. The simulation was compared to two other models: a Weibull distribution model, and an AR model with normally distributed residuals. The statistical fit with the SCADA data was found to be closer than these two models. Spatial correlation using only the MERRA data was found to be higher than the SCADA data, indicating that there is still a further source of variability to be accounted for. Hence the simulation spatial correlation was calibrated to previously reported findings, which were similar to the SCADA data. MonthWind speed (m/s) Daily average wind speed (m/s) Normalised frequency