The impact of short duration, high intensity exercise on cardiac troponin release

The aim of this study was to assess the appearance of cardiac troponins (cTnI and/or cTnT) after a short bout (30 s) of ‘all-out’ intense exercise and to determine the stability of any exercise-related cTnI release in response to repeated bouts of high intensity exercise separated by 7 days recovery. Eighteen apparently healthy, physically active, male university students completed two all-out 30 s cycle sprint, separated by 7 days. cTnI, blood lactate and catecholamine concentrations were measured before, immediately after and 24 h after each bout. Cycle performance, heart rate and blood pressure responses to exercise were also recorded. Cycle performance was modestly elevated in the second trial [6·5% increase in peak power output (PPO)]; there was no difference in the cardiovascular, lactate or catecholamine response to the two cycle trials. cTnI was not significantly elevated from baseline through recovery (Trial 1: 0·06 ± 0·04 ng ml−1, 0·05 ± 0·04 ng ml−1, 0·03 ± 0·02 ng ml−1; Trial 2: 0·02 ± 0·04 ng ml−1, 0·04 ± 0·03 ng ml−1, 0·05 ± 0·06 ng ml−1) in either trial. Very small within subject changes were not significantly correlated between the two trials (r = 0·06; P>0·05). Subsequently, short duration, high intensity exercise does not elicit a clinically relevant response in cTnI and any small alterations likely reflect the underlying biological variability of cTnI measurement within the participants.

A genetic algorithm based economic dispatch (GAED) with environmental constraint optimisation

The role of renewable energy in power systems is becoming more significant due to the increasing cost of fossil fuels and climate change concerns. However, the inclusion of Renewable Energy Generators (REG), such as wind power, has created additional problems for power system operators due to the variability and lower predictability of output of most REGs, with the Economic Dispatch (ED) problem being particularly difficult to resolve. In previous papers we had reported on the inclusion of wind power in the ED calculations. The simulation had been performed using a system model with wind power as an intermittent source, and the results of the simulation have been compared to that of the Direct Search Method (DSM) for similar cases. In this paper we report on our continuing investigations into using Genetic Algorithms (GA) for ED for an independent power system with a significant amount of wind energy in its generator portfolio. The results demonstrate, in line with previous reports in the literature, the effectiveness of GA when measured against a benchmark technique such as DSM.