Sven Sigurdsson

The chemistry of geothermal waters in Iceland. I. Calculation of aqueous speciation from 0° to 370°C

Abstract A computer programme has been developed to calculate the composition and aqueous speciation of geothermal reservoir waters including pH, redox potential and gas partial pressures. The programme is specifically suited to handle geochemical data from wet-steam wells, hot-water wells and boiling hot springs, but it may also be used for non-thermal waters. Solubility data for selected geothermal minerals are incorporated to facilitate the study of solution mineral equilibria. The programme may also be used to study chemical changes in water chemistry accompanying boiling, variable degassing and cooling, and how these changes disturb solution mineral equilibria.

Reliability of quantitative EEG features

OBJECTIVE To investigate the reliability of several well-known quantitative EEG (qEEG) features in the elderly in the resting, eyes closed condition and study the effects of epoch length and channel derivations on reliability. METHODS Fifteen healthy adults, over 50 years of age, underwent 10 EEG recordings over a 2-month period. Various qEEG features derived from power spectral, coherence, entropy and complexity analysis of the EEG were computed. Reliability was quantified using an intraclass correlation coefficient. RESULTS The highest reliability was obtained with the average montage, reliability increased with epoch length up to 40s, longer epochs gave only marginal improvement. The reliability of the qEEG features was highest for power spectral parameters, followed by regularity measures based on entropy and complexity, coherence being least reliable. CONCLUSIONS Montage and epoch length had considerable effects on reliability. Several apparently unrelated regularity measures had similar stability. Reliability of coherence measures was strongly dependent on channel location and frequency bands. SIGNIFICANCE The reliability of regularity measures has until now received limited attention. Low reliability of coherence measures in general may limit their usefulness in the clinical setting.

Aquifer chemistry of four high-temperature geothermal systems in Iceland

Abstract The deep water feeding wet-steam wells in four high-temperature geothermal areas in Iceland have highly variable salinity as reflected in the chlorine concentrations which vary from 20 to 19000 ppm. Using available values for equilibrium constants, the activities of 26 chemical species involving the major components of the reservoir water have been calculated and quantitative evaluations of solute/ solute, mineral/solute chemical equilibria in these geothermal systems have been made. The unflashed reservoir water is just saturated with calcite. The saline geothermal waters, which represent heated sea-water, are just saturated with anhydrite, but the dilute waters, which are of meteoric origin, are somewhat undersaturated with this mineral. The fluoride mobility is thought to be limited by an ionic exchange reaction where F− replaces some of the OH− in the layered silicates. The pH of the unflashed reservoir water is governed by ionic exchange equilibrium in which all the major cations participitate. At a given temperature it seems likely that the activity of one cation fixes the activities of all the other major cations and hydrogen ion. If this is so and we take all the other chemical equilibria which have been demonstrated to exist for granted, it turns out that the major element composition of the unflashed high-temperature geothermal waters is controlled by two independent variables only. These variables are the temperature and the supply to the water of the incompatible element chlorine, incompatible indicating that this element is not incorporated in the geothermal minerals.

Support vector machines and dynamic time warping for time series

Effective use of support vector machines (SVMs) in classification necessitates the appropriate choice of a kernel. Designing problem specific kernels involves the definition of a similarity measure, with the condition that kernels are positive semi-definite (PSD). An alternative approach which places no such restrictions on the similarity measure is to construct a set of inputs and let each example be represented by its similarity to all the examples in this set and then apply a conventional SVM to this transformed data. Dynamic time warping (DTW) is a well established distance measure for time series but has been of limited use in SVMs since it is not obvious how it can be used to derive a PSD kernel. The feasibility of the similarity based approach for DTW is investigated by applying the method to a large set of time-series classification problems.

Multistep Methods with Variable Matrix Coefficients

In a previous paper concerning linear multistep methods with mildly varying coefficients for the numerical solution of a single ordinary differential equation, a certain stabilizing condition was derived. Satisfaction of this condition ensured, under certain hypotheses, that the parasitic solutions of the difference equation which arise when such a method is applied to a nonlinear differential equation would not dominate the genuine solution. In this paper, it is shown that the application of a generalization of this stabilizing condition to a somewhat wider class of linear multistep methods—which are applicable to systems of differential equations—results in a class of methods with variable matrix coefficients. It is shown that methods of this class, with suitably chosen coefficients, possess a stability property similar to A-stability, and the asymptotic stability behavior of the methods when applied to a variable coefficient linear system is also investigated. Explicit and implicit examples of the new ...

Automatic Sleep Staging using Support Vector Machines with Posterior Probability Estimates

This paper describes attempts at constructing an automatic sleep stage classifier using EEG recordings. Three different feature extraction schemes were compared together with two different pattern classifiers, the recently introduced support vector machine and the well known k-nearest neighbor classifier. Using estimates of posterior probabilities for each of the sleep stages it was possible to devise a simple post-processing rule which leads to improved accuracy. Compared to a human expert the accuracy of the best classifier is 81%

The feasibility of constructing a Predictive Outcome Model for breast cancer using the tools of data mining

A Predictive Outcome Model (POM) for breast cancer was built, and its ability to accurately predict the (5 year) outcome of an incidence of cancer was assessed. A wide range of different feature selection and classification methods were applied in order to find the best performing algorithms on a given dataset. A special Model Selection Tool, MST, was developed to facilitate the search for the most efficient classifier model. The MST includes programs for choosing different classification algorithms, selecting subsets of features, dealing with imbalance in the data and evaluating the predictive performance by various measures. These steps are important in most data mining tasks and it would be time consuming to conduct them manually. The dataset, Rose, was assembled retroactively for this study and contains data records from 257 women diagnosed with primary breast cancer in Iceland during the years 1996-1998. An extra feature, containing the risk assessment of a doctor was added to the dataset which initially contained 400 features, both to see how much that could enhance the performance of the model and to investigate to what extent such a subjective assessment can be predicted from the remaining features. The main result is that similar performance is achieved regardless of which algorithm is used. Furthermore, the inclusion of the doctors assessment does not appear to significantly enhance the performance. That is also reflected in the fact that the models are in general more successful in predicting the doctors risk assessment than the actual outcome if resulting Kappa values are compared.

On-line Detection of Patient Specific Neonatal Seizures using Support Vector Machines and Half-Wave Attribute Histograms

An efficient and effective support vector machine for online seizures detection is presented. The kernel designed is based on features generated from bivariate histograms of EEG half-wave attributes. The training is online using a simple heuristic known as chunking. The case study presented illustrates the performance of the method on typical neonatal seizures

Dynamics of group formation in collective motion of organisms

A mathematical description of the collective motion of organisms using a density-velocity model is presented. This model consists of a system of nonlinear parabolic equations, a forced Burgers equation for velocity and a diffusion-convection equation for density. The motion is mainly due to forces resulting from the differences between local density levels and a prescribed density level. The existence of a global attractor for a 1D density-velocity model is proved by asymptotic analysis to demonstrate different patterns in the attractors for density. The theoretical results are supplemented with numerical results. These patterns correspond to movements of collective organized groups of organisms such as fish schools and bird flocks.

Numerical modelling and experimental investigation of drug release from layered silicone matrix systems

Medical devices and polymeric matrix systems that release drugs or other bioactive compounds are of interest for a variety of applications. The release of the drug can be dependent on a number of factors such as the solubility, diffusivity, dissolution rate and distribution of the solid drug in the matrix. Achieving the goal of an optimal release profile can be challenging when relying solely on traditional experimental work. Accurate modelling complementing experimentation is therefore desirable. Numerical modelling is increasingly becoming an integral part of research and development due to the significant advances in computer simulation technology. This work focuses on numerical modelling and investigation of multi-layered silicone matrix systems. A numerical model that can be used to model multi-layered systems was constructed and validated by comparison with experimental data. The model could account for the limited dissolution rate and effect of the drug distribution on the release profiles. Parametric study showed how different factors affect the characteristics of drug release. Multi-layered medical silicone matrices were prepared in special moulds, where the quantity of drug in each layer could be varied, and release was investigated with Franz-diffusion cell setup. Data for long-term release was fitted to the model and the full depletion of the system predicted. The numerical model constructed for this study, whose input parameters are the diffusion, effective dissolution rate and dimensional solubility coefficients, does not require any type of steady-state approximation. These results indicate that numerical model can be used as a design tool for development of controlled release systems such as drug-loaded medical devices.