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At least 55 records · Page 3Linked to original sources
PubMed1992

Electrical impedance tomography using induced and injected currents.

A two-dimensional forward problem formulation is introduced for electrical impedance tomography (EIT) using induced currents. The forward problem is linearised around a certain resistivity distribution and the inverse problem is formulated as a solution of a linear system of equations. Sensitivity of boundary measurements to resistivity variations are analysed for spatially uniform, linear and quadratic fields. The formulation, however, is suitable for studying the effects of a general magnetic field applied to induce the currents in the conductive object. A similar inverse problem formulation is also developed for EIT using injected currents. Simulation studies are performed by reconstructing images of a simulation distribution using both methods separately with generalised inversion. It is also shown that the derived formulations for the inverse problems of the two methods can be combined to solve a larger set of equations with a greater number of independent measurements.

Electric Conductivity↗
PubMed2005

Sources of uncertainty in model predictions: lessons learned from the IAEA Forest and Fruit Working Group model intercomparisons.

The International Atomic Energy Agency (IAEA), through the BIOMASS program, has provided a unique international forum for assessing the relative contribution of different sources of uncertainty associated with environmental modeling. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte-Carlo modeling are often recommended. The issue of model uncertainty is still rarely addressed in practical applications and the use of several alternative models to derive a range of model outputs (similar to what was done in IAEA model intercomparisons) is one of a few available techniques. This paper addresses the often overlooked issue of what we call 'modeler uncertainty,' i.e., differences in problem formulation, model implementation and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit and Forest Working Groups created under the BIOMASS program (BIOsphere Modeling and ASSessment). The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that the parameter uncertainty (as evaluated by a probabilistic Monte-Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in fate and transport modeling and risk characterization.

Fruit↗
PubMed2003

Model uncertainty and choices made by modelers: lessons learned from the International Atomic Energy Agency model intercomparisons.

The treatment of uncertainties associated with modeling and risk assessment has recently attracted significant attention. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte Carlo modeling are often recommended. However, the issue of model uncertainty is still rarely addressed in practical applications of risk assessment. The use of several alternative models to derive a range of model outputs or risks is one of a few available techniques. This article addresses the often-overlooked issue of what we call "modeler uncertainty," i.e., difference in problem formulation, model implementation, and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit Working Group, which was created under the International Atomic Energy Agency (IAEA) BIOMASS program (BIOsphere Modeling and ASSessment). Model-model and model-data intercomparisons reviewed in this study were conducted by the working group for a total of three different scenarios. The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that parameter uncertainty (as evaluated by a probabilistic Monte Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in risk characterization.

Journal Article↗
PubMed2003

Evolutionary algorithms for the selection of single nucleotide polymorphisms.

BACKGROUND: Large databases of single nucleotide polymorphisms (SNPs) are available for use in genomics studies. Typically, investigators must choose a subset of SNPs from these databases to employ in their studies. The choice of subset is influenced by many factors, including estimated or known reliability of the SNP, biochemical factors, intellectual property, cost, and effectiveness of the subset for mapping genes or identifying disease loci. We present an evolutionary algorithm for multiobjective SNP selection. RESULTS: We implemented a modified version of the Strength-Pareto Evolutionary Algorithm (SPEA2) in Java. Our implementation, Multiobjective Analyzer for Genetic Marker Acquisition (MAGMA), approximates the set of optimal trade-off solutions for large problems in minutes. This set is very useful for the design of large studies, including those oriented towards disease identification, genetic mapping, population studies, and haplotype-block elucidation. CONCLUSION: Evolutionary algorithms are particularly suited for optimization problems that involve multiple objectives and a complex search space on which exact methods such as exhaustive enumeration cannot be applied. They provide flexibility with respect to the problem formulation if a problem description evolves or changes. Results are produced as a trade-off front, allowing the user to make informed decisions when prioritizing factors. MAGMA is open source and available at http://snp-magma.sourceforge.net. Evolutionary algorithms are well suited for many other applications in genomics.

Algorithms↗
PubMed2000

Integrating human health and ecological concerns in risk assessments.

The interconnections between ecosystems, human health and welfare have been increasingly recognized by the US government, academia, and the public. This paper continues this theme by addressing the use of risk assessment to integrate people into a single assessment. In a broad overview of the risk assessment process we stress the need to build a conceptual model of the whole system including multiple species (humans and other ecological entities), stressors, and cumulative effects. We also propose converging landscape ecology and evaluation of ecosystem services with risk assessment to address these cumulative responses. We first look at how this integration can occur within the problem formulation step in risk assessment where the system is defined, a conceptual model created, a subset of components and functions selected, and the analytical framework decided in a context that includes the management decisions. A variety of examples of problem formulations (salmon, wild insects, hyporheic ecosystems, ultraviolet (UV) radiation, nitrogen fertilization, toxic chemicals, and oil spills) are presented to illustrate how treating humans as components of the landscape can add value to risk assessments. We conclude that the risk assessment process should help address the urgent needs of society in proportion to importance, to provide a format to communicate knowledge and understanding, and to inform policy and management decisions.

Animals↗
PubMed2006

Risk- and robustness-based solutions to a multi-objective water distribution system rehabilitation problem under uncertainty .

The water distribution system (WDS) rehabilitation problem is defined here as a multi-objective optimisation problem under uncertainty. Two alternative problem formulations are considered. The first objective in both approaches is to minimise the total rehabilitation cost. The second objective is to either maximise the overall WDS robustness or to minimise the total WDS risk. The WDS robustness is defined as the probability of simultaneously satisfying minimum pressure head constraints at all nodes in the network. Total risk is defined as the sum of nodal risks, where nodal risk is defined as the product of the probability of pressure failure at that node and consequence of such failure. Decision variables are the alternative rehabilitation options for each pipe in the network. The only source of uncertainty is the future water consumption. Uncertain demands are modelled using any probability density functions (PDFs) assigned in the problem formulation phase. The corresponding PDFs of the analysed nodal heads are calculated using the Latin Hypercube sampling technique. The optimal rehabilitation problem is solved using the newly developed rNSGAII method which is a modification of the well-known NSGAII optimisation algorithm. In rNSGAII a small number of demand samples are used for each fitness evaluation leading to significant computational savings when compared to the full sampling approach. The two alternative approaches are tested, verified and their performance compared on the New York tunnels case study. The results obtained demonstrate that both new methodologies are capable of identifying the robust (near) Pareto optimal fronts while making significant computational savings.

Facility Design and Construction↗
PubMed2002

Improved Monte Carlo sampling in a real space approach to the crystallographic phase problem.

A real space approach has been proposed to solve the x-ray phase problem formulated as a minimization problem. The cost function consists of two parts: one represents the usual crystallographical residual while the other enforces the probability distribution of the invariant phase triplets. Starting from a random real space structure, the atoms are moved one by one to gradually reduce the cost function (simulated annealing). In addition, the atoms are encouragd to preferentially sample the high density regions in space determined by an approximate density map which in turn is updated and modified by averaging and Fourier synthesis. Such a reduction of the configurational space has led to considerable improvement of the algorithm compared to an earlier version. Trial calculations for structures including hexadecaisoleucinomycin (HEXIL) and a collagenlike peptide (PPG) are presented.

Journal Article↗
PubMed2000

A hybrid minimal principle for the crystallographic phase problem.

Simulated annealing is used to solve the X-ray phase problem formulated as a minimization problem. The cost function consists of two parts, one represents the discrepancy between measured and calculated intensities while the other monitors the probability distribution of the triplets. From a random real-space structure at the start, the atoms are moved one by one to gradually reduce the cost function until the best structure emerges. Trial calculations for structures including hexadecaisoleucinomycin (HEXIL) are presented. Comparison of this method with other related methods is made.

Crystallography, X-Ray↗