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.

Dynamic load balancing for the distributed mining of molecular structures

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

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.

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.

Generic systolic array for genetic algorithms

The authors present a systolic design for a simple GA mechanism which provides high throughput and unidirectional pipelining by exploiting the inherent parallelism in the genetic operators. The design computes in O(N+G) time steps using O(N2) cells where N is the population size and G is the chromosome length. The area of the device is independent of the chromosome length and so can be easily scaled by replicating the arrays or by employing fine-grain migration. The array is generic in the sense that it does not rely on the fitness function and can be used as an accelerator for any GA application using uniform crossover between pairs of chromosomes. The design can also be used in hybrid systems as an add-on to complement existing designs and methods for fitness function acceleration and island-style population management

Parallel genetic algorithms for the tuning of a fuzzy AQM controller

This paper presents the results of the application of a parallel Genetic Algorithm (GA) in order to design a Fuzzy Proportional Integral (FPI) controller for active queue management on Internet routers. The Active Queue Management (AQM) policies are those policies of router queue management that allow the detection of network congestion, the notification of such occurrences to the hosts on the network borders, and the adoption of a suitable control policy. Two different parallel implementations of the genetic algorithm are adopted to determine an optimal configuration of the FPI controller parameters. Finally, the results of several experiments carried out on a forty nodes cluster of workstations are presented.