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.

A contour-advective semi-lagrangian numerical algorithm for simulating fine-scale conservative dynamical fields

This paper describes a novel numerical algorithm for simulating the evolution of fine-scale conservative fields in layer-wise two-dimensional flows, the most important examples of which are the earth's atmosphere and oceans. the algorithm combines two radically different algorithms, one Lagrangian and the other Eulerian, to achieve an unexpected gain in computational efficiency. The algorithm is demonstrated for multi-layer quasi-geostrophic flow, and results are presented for a simulation of a tilted stratospheric polar vortex and of nearly-inviscid quasi-geostrophic turbulence. the turbulence results contradict previous arguments and simulation results that have suggested an ultimate two-dimensional, vertically-coherent character of the flow. Ongoing extensions of the algorithm to the generally ageostrophic flows characteristic of planetary fluid dynamics are outlined.

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.

A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances.

A genetic algorithm for the design of a fuzzy controller for active queue management

Active queue management (AQM) policies are those policies of router queue management that allow for 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. This paper proposes the adoption of a fuzzy proportional integral (FPI) controller as an active queue manager for Internet routers. The analytical design of the proposed FPI controller is carried out in analogy with a proportional integral (PI) controller, which recently has been proposed for AQM. A genetic algorithm is proposed for tuning of the FPI controller parameters with respect to optimal disturbance rejection. In the paper the FPI controller design metodology is described and the results of the comparison with random early detection (RED), tail drop, and PI controller are presented.

Scalability of efficient parallel K-Means

Clustering is defined as the grouping of similar items in a set, and is an important process within the field of data mining. As the amount of data for various applications continues to increase, in terms of its size and dimensionality, it is necessary to have efficient clustering methods. A popular clustering algorithm is K-Means, which adopts a greedy approach to produce a set of K-clusters with associated centres of mass, and uses a squared error distortion measure to determine convergence. Methods for improving the efficiency of K-Means have been largely explored in two main directions. The amount of computation can be significantly reduced by adopting a more efficient data structure, notably a multi-dimensional binary search tree (KD-Tree) to store either centroids or data points. A second direction is parallel processing, where data and computation loads are distributed over many processing nodes. However, little work has been done to provide a parallel formulation of the efficient sequential techniques based on KD-Trees. Such approaches are expected to have an irregular distribution of computation load and can suffer from load imbalance. This issue has so far limited the adoption of these efficient K-Means techniques in parallel computational environments. In this work, we provide a parallel formulation for the KD-Tree based K-Means algorithm and address its load balancing issues.

Efficient operator pipelining in a bit serial genetic algorithm engine

The authors propose a bit serial pipeline used to perform the genetic operators in a hardware genetic algorithm. The bit-serial nature of the dataflow allows the operators to be pipelined, resulting in an architecture which is area efficient, easily scaled and is independent of the lengths of the chromosomes. An FPGA implementation of the device achieves a throughput of >25 million genes per second