The Effect of TiO2 Nanoparticles on the Aquatic Ecosystem: A Comparative Ecotoxicity Study with Test Organisms of Different Trophic Levels

A comprehensive ecotoxicological assessment was carried out with Degussa VP nano TiO2 suspension applying a bioluminescent bacterium (Aliivibrio fischeri), algae (Pseudokirchneriella subcapitata, Scenedesmus subspicatus and Chlorella vulgaris), a protozoon (Tetrahymena pyriformis), the water flea (Daphnia magna) and an aquatic macrophyte, Lemna minor. TiO2 nanoparticles were toxic in the set of the conducted tests, but the toxicity level varied with the organisms and endpoints. According to our results the concentrations, the duration and the mechanisms of exposure are contributing factors to the toxicity of nanoparticles. The Tetrahymena phagocytic activity, the Daphnia heartbeat rate and the Lemna total chlorophyll content as ecotoxicity endpoints showed outstanding sensitivity. These organisms showed significant behavioural and physiological changes when exposed to low TiO2 nanoparticle concentrations (0.1 and 0.05 µg/L) considered to be lower than the predicted environmental concentration in surface waters. These results reveal the importance of behavioural and physiological assays in assessing the impact of nanoparticles and indicate that nanosized TiO2 may pose risks to the aquatic ecosystem

Multi-job Meta-Brokering in Distributed Computing Infrastructures using Pliant Logic

The ever growing number of computation- intensive applications calls for the interoperation of distributed infrastructures such as Clouds, Grids and private clusters. The European SHIWA and ER-flow projects have been initiated to enable the combination of heterogeneous scientific work- flows, and to execute them in a large-scale system consisting of multiple Distributed Computing Infrastructures including Grids and Clouds. In this paper we focus on one of the resource management challenges of these projects called multi- job scheduling. A parameter study job of a workflow having a large number of input files to be consumed by independent job instances is called a multi-job. In order to cope with the high uncertainty and unpredictable load of these infrastructures and with the simultaneous submissions of multi-job instances, we use statistical historical job allocation data gathered from real-world workflow archives and propose an adaptive meta- brokering approach for the management of this unified system based on the Pliant logic concept, which is a specific part of fuzzy logic theory. We argue that this novel scheduling technique produce better performance scores, hence the overall load of the system can be more balanced