Enhanced phytoextraction: In search of EDTA alternatives

Enhanced phytoextraction proposes the use of soil amendments to increase the heavy-metal content of above-ground harvestable plant tissues. This study compares the effect of synthetic aminopolycarboxylic acids [ethylenediamine tetraacetatic acid (EDTA), nitriloacetic acid (NTA), and diethylenetriamine pentaacetic acid (DTPA)] with a number of biodegradable, low-molecular weight, organic acids (citric acid, ascorbic acid, oxalic acid, salicylic acid, and NH4 acetate) as potential soil amendments for enhancing phytoextraction of heavy metals (Cu, Zn, Cd, Pb, and Ni) by Zea mays. The treatments in this study were applied at a dose of 2 mmol/kg(-1) 1 d before sowing. To compare possible effects between presow and postgermination treatments, a second smaller experiment was conducted in which EDTA, citric acid, and NH4 acetate were added 10 d after germination as opposed to 1 d before sowing. The soil used in this screening was a moderately contaminated topsoil derived from a dredged sediment disposal site. This site has been in an oxidized state for more than 8 years before being used in this research. The high carbonate, high organic matter, and high clay content characteristic to this type of sediment are thought to suppress heavy-metal phytoavailability. Both EDTA and DTPA resulted in increased levels of heavy metals in the above-ground biomass. However, the observed increases in uptake were not as large as reported in the literature. Neither the NTA nor organic acid treatments had any significant effect on uptake when applied prior to sowing. This was attributed to the rapid mineralization of these substances and the relatively low doses applied. The generally low extraction observed in this experiment restricts the use of phytoextraction as an effective remediation alternative under the current conditions, with regard to amendments used, applied dose (2 mmol/kg(-1) soil), application time (presow), plant species (Zea mays), and sediment (calcareous clayey soil) under study.

Decentralized load balancing for highly irregular search problems

In this paper, we present a distributed computing framework for problems characterized by a highly irregular search tree, whereby no reliable workload prediction is available. The framework is based on a peer-to-peer computing environment and dynamic load balancing. The system allows for dynamic resource aggregation, does not depend on any specific meta-computing middleware and is suitable for large-scale, multi-domain, heterogeneous environments, such as computational Grids. Dynamic load balancing policies based on global statistics are known to provide optimal load balancing performance, while randomized techniques provide high scalability. The proposed method combines both advantages and adopts distributed job-pools and a randomized polling technique. The framework has been successfully adopted in a parallel search algorithm for subgraph mining and evaluated on a molecular compounds dataset. The parallel application has shown good calability and close-to linear speedup in a distributed network of workstations.

Gentlemen, stop your engines!

For fifty years, computer chess has pursued an original goal of Artificial Intelligence, to produce a chess-engine to compete at the highest level. The goal has arguably been achieved, but that success has made it harder to answer questions about the relative playing strengths of man and machine. The proposal here is to approach such questions in a counter-intuitive way, handicapping or stopping-down chess engines so that they play less well. The intrinsic lack of man-machine games may be side-stepped by analysing existing games to place computer engines as accurately as possible on the FIDE ELO scale of human play. Move-sequences may also be assessed for likelihood if computer-assisted cheating is suspected.

Database Engines for Geographical Information Systems

Our ability to identify, acquire, store, enquire on and analyse data is increasing as never before, especially in the GIS field. Technologies are becoming available to manage a wider variety of data and to make intelligent inferences on that data. The mainstream arrival of large-scale database engines is not far away. The experience of using the first such products tells us that they will radically change data management in the GIS field.

In search of simple structures in climate: Simplifying EOFs

Empirical orthogonal functions (EOFs) are widely used in climate research to identify dominant patterns of variability and to reduce the dimensionality of climate data. EOFs, however, can be difficult to interpret. Rotated empirical orthogonal functions (REOFs) have been proposed as more physical entities with simpler patterns than EOFs. This study presents a new approach for finding climate patterns with simple structures that overcomes the problems encountered with rotation. The method achieves simplicity of the patterns by using the main properties of EOFs and REOFs simultaneously. Orthogonal patterns that maximise variance subject to a constraint that induces a form of simplicity are found. The simplified empirical orthogonal function (SEOF) patterns, being more 'local'. are constrained to have zero loadings outside the main centre of action. The method is applied to winter Northern Hemisphere (NH) monthly mean sea level pressure (SLP) reanalyses over the period 1948-2000. The 'simplified' leading patterns of variability are identified and compared to the leading patterns obtained from EOFs and REOFs. Copyright (C) 2005 Royal Meteorological Society.