SUMMARY: Many natural nucleic acid sequences have evolutionarily conserved secondary structures with diverse biological functions. A reliable computational tool for identifying such structures would be very useful in guiding experimental analyses of their biological functions. NASP (Nucleic Acid Structure Predictor) is a program that takes into account thermodynamic stability, Boltzmann base pair probabilities, alignment uncertainty, covarying sites and evolutionary conservation to identify biologically relevant secondary structures within multiple sequence alignments. Unique to NASP is the consideration of all this information together with a recursive permutation-based approach to progressively identify and list the most conserved probable secondary structures that are likely to have the greatest biological relevance. By focusing on identifying only evolutionarily conserved structures, NASP forgoes the prediction of complete nucleotide folds but outperforms various other secondary structure prediction methods in its ability to selectively identify actual base pairings. AVAILABILITY: Downloable and web-based versions of NASP are freely available at http://web.cbio.uct.ac.za/~yves/nasp_portal.php CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Power system networks are complex systems that are highly nonlinear and non-stationary, and therefore, their performance is difficult to optimize using traditional optimization techniques. This paper presents an enhanced particle swarm optimizer for solving constrained optimization problems for power system applications, in particular, the optimal allocation of multiple STATCOM units. The study focuses on the capability of the algorithm to find feasible solutions in a highly restricted hyperspace. The performance of the enhanced particle swarm optimizer is compared with the classical particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and bacterial foraging algorithm (BFA). Results show that the enhanced PSO is able to find feasible solutions faster and converge to feasible regions more often as compared with other algorithms. Additionally, the enhanced PSO is capable of finding the global optimum without getting trapped in local minima.