Biomedical subjects

Jasper A Vrugt

Publications and source records attributed to Jasper A Vrugt.

4 recordsLinked to original sources

PubMed2007

Improved evolutionary optimization from genetically adaptive multimethod search.

In the last few decades, evolutionary algorithms have emerged as a revolutionary approach for solving search and optimization problems involving multiple conflicting objectives. Beyond their ability to search intractably large spaces for multiple solutions, these algorithms are able to maintain a diverse population of solutions and exploit similarities of solutions by recombination. However, existing theory and numerical experiments have demonstrated that it is impossible to develop a single algorithm for population evolution that is always efficient for a diverse set of optimization problems. Here we show that significant improvements in the efficiency of evolutionary search can be achieved by running multiple optimization algorithms simultaneously using new concepts of global information sharing and genetically adaptive offspring creation. We call this approach a multialgorithm, genetically adaptive multiobjective, or AMALGAM, method, to evoke the image of a procedure that merges the strengths of different optimization algorithms. Benchmark results using a set of well known multiobjective test problems show that AMALGAM approaches a factor of 10 improvement over current optimization algorithms for the more complex, higher dimensional problems. The AMALGAM method provides new opportunities for solving previously intractable optimization problems.

Algorithms↗

PubMed2005

Sustainability of irrigated agriculture in the San Joaquin Valley, California.

The sustainability of irrigated agriculture in many arid and semiarid areas of the world is at risk because of a combination of several interrelated factors, including lack of fresh water, lack of drainage, the presence of high water tables, and salinization of soil and groundwater resources. Nowhere in the United States are these issues more apparent than in the San Joaquin Valley of California. A solid understanding of salinization processes at regional spatial and decadal time scales is required to evaluate the sustainability of irrigated agriculture. A hydro-salinity model was developed to integrate subsurface hydrology with reactive salt transport for a 1,400-km(2) study area in the San Joaquin Valley. The model was used to reconstruct historical changes in salt storage by irrigated agriculture over the past 60 years. We show that patterns in soil and groundwater salinity were caused by spatial variations in soil hydrology, the change from local groundwater to snowmelt water as the main irrigation water supply, and by occasional droughts. Gypsum dissolution was a critical component of the regional salt balance. Although results show that the total salt input and output were about equal for the past 20 years, the model also predicts salinization of the deeper aquifers, thereby questioning the sustainability of irrigated agriculture.

Agriculture↗

PubMed2004

(Un)certainty of overall binding constants of Al with dissolved organic matter determined by the Scatchard approach.

One of the best approaches to date to obtain overall binding constants (Ko) for Al and dissolved organic matter (DOM) from acidic soil solutions is to collect 'free' Al data with diffusive gradients in thin films (DGT) and to infer the Ko values by fitting a continuous distribution model based on Scatchard plots. Although there is clear established literature demonstrating the usefulness of the Scatchard approach, relatively little attention has been given to a realistic assessment of the uncertainties associated with the final fitted Ko values. In this study we present an uncertainty analysis of the fitted Ko values using a synthetic dataset with different levels of random noise and a real data set using DGT data from an acidic soil solution. The parameters in the continuous distribution model and their corresponding upper and lower 95% uncertainty bounds were determined using the Shuffled Complex Evolution Metropolis (SCEM) algorithm. Although reasonable fits of the distribution model to the experimental data were obtained in all cases, an appreciable uncertainty in the resulting Ko values was found due to three main reasons. Firstly, obtaining 'free' Al data even with the DGT method is relatively difficult, leading to uncertainty in the data. Secondly, before Scatchard plots can be constructed, the maximum binding capacity (MBC) must be estimated. Any uncertainty in this MBC propagates into uncertainty associated with the final plots. Thirdly, as the final fitted Ko values are largely based on extrapolation, a small uncertainty in the fit of the binding data results in an appreciable uncertainty in the obtained Ko. Therefore, while trends in Ko for Al and DOM could easily be discerned and compared, the uncertainty in the Ko values hinders the application in quantitative speciation calculation. More comprehensive speciation models that avoid the use of Ko seem to fit better for this purpose.

Aluminum↗

PubMed2002

Copper complexation by dissolved organic matter and uncertainty assessment of their stability constants.

The interaction of Cu with dissolved organic matter (DOM, extracted from an organic forest floor) was investigated and the resulting data was evaluated in terms of their uncertainty. The speciation of Cu over 'free' Cu (as analysed by diffusive gradients in thin films (DGT)), dissolved Cu-DOM complexes and precipitated Cu-DOM was determined as a function of pH (3.5, 4.0 and 4.5) and Cu/C ratio. The dissolved organically bound fraction was highest at pH 4.5, but this fraction decreased with increasing Cu/C ratio, which was observed for all pH levels. In the range of Cu/C = 7 x 10(-5) -2.3 x 10(-2) (mol/mol) the precipitated fraction was very small. The speciation of both Al and Fe was not affected by increasing Cu concentrations. From a continuous distribution model using the Scatchard approach, we calculated the optimal fit and corresponding upper and lower 95% uncertainty bounds of the overall stability constants (K(o)) with the shuffled complex evolution Metropolis (SCEM) algorithm. Although the optimal equation fitted the data very well, the uncertainty of the, according to literature, most reliable approach to establish stability constants, was still large. Accordingly, the usually reported intrinsic stability constants exhibited large uncertainty ranging from log K(i) = 6.0-7.1 (optimal 6.7) for pH 3.5, log K(i) = 6.5-7.1 (optimal 6.8) for pH 4.0, and log K(i) = 6.4-7.2 (optimal 6.8) for pH 4.5 and showed only little effect of pH.

Chemical Precipitation↗