Taxonomic Identity in Microbial Eukaryotes: A Practical Approach Using the Testate Amoeba Centropyxis to Resolve Conflicts Between Old and New Taxonomic Descriptions

The present work focuses on 12 taxa of the genus Centropyxis Stein, 1857 to explore the conflict between traditional and contemporary taxonomic practices. We examined the morphology, biometry, and ecology of 2,120 Centropyxis individuals collected from Tiete River, Sao Paulo, Brazil; with these new data we studied the consistency of previously described species, varieties, and forms. We encountered transitional forms of test morphology that undermine specific and varietal distinctions for three species and nine varieties. Biometrical analyses made comparing the organisms at the species level suggest a lack of separation between Centropyxis aculeata and Centropyxis discoides, and a possible distinction for Centropyxis ecornis based on spine characteristics. However, incongruence between recent and previous surveys makes taking any taxonomic-nomenclatural actions inadvisable, as they would only add to the confusion. We suggest an explicit and objective taxonomic practice in order to enhance our taxonomic and species concepts for microbial eukaryotes. This will allow more precise inferences of taxon identity for studies in other areas.

Job rotation in assembly lines employing disabled workers

In this paper we consider the programming of job rotation in the assembly line worker assignment and balancing problem. The motivation for this study comes from the designing of assembly lines in sheltered work centers for the disabled, where workers have different task execution times. In this context, the well-known training aspects associated with job rotation are particularly desired. We propose a metric along with a mixed integer linear model and a heuristic decomposition method to solve this new job rotation problem. Computational results show the efficacy of the proposed heuristics. (C) 2009 Elsevier B.V. All rights reserved.

Grid job scheduling using Route with Genetic Algorithm support

In 2006 the Route load balancing algorithm was proposed and compared to other techniques aiming at optimizing the process allocation in grid environments. This algorithm schedules tasks of parallel applications considering computer neighborhoods (where the distance is defined by the network latency). Route presents good results for large environments, although there are cases where neighbors do not have an enough computational capacity nor communication system capable of serving the application. In those situations the Route migrates tasks until they stabilize in a grid area with enough resources. This migration may take long time what reduces the overall performance. In order to improve such stabilization time, this paper proposes RouteGA (Route with Genetic Algorithm support) which considers historical information on parallel application behavior and also the computer capacities and load to optimize the scheduling. This information is extracted by using monitors and summarized in a knowledge base used to quantify the occupation of tasks. Afterwards, such information is used to parameterize a genetic algorithm responsible for optimizing the task allocation. Results confirm that RouteGA outperforms the load balancing carried out by the original Route, which had previously outperformed others scheduling algorithms from literature.