Hybrid genetic algorithm–tabu search approach for optimising multilayer optical coatings

Constructing multilayer optical coatings (MOCs) is a difficult large-scale optimisation problem due to the enormous size of the search space. In the present paper, a new approach for designing MOCs is presented using genetic algorithms (GAs) and tabu search (TS). In this approach, it is not necessary to specify how many layers will be present in a design, only a maximum needs to be defined. As it is generally recognised that the existence of specific repeating blocks is beneficial for a design, a specific GA representation of a design is used which promotes the occurrence of repeating blocks. Solutions found by GAs are improved by a new refinement method, based on TS, a global optimisation method which is loosely based on artificial intelligence. The improvements are demonstrated by creating a visible transmitting/infrared reflecting filter with a wide variety of materials.     

The influence of different structure representations on the clustering of an RNA nucleotides data set

The last couple of years an overwhelming amount of data has emerged in the field of biomolecular structure determination. To explore information hidden in these structure databases, clustering techniques can be used. The outcome of the clustering experiments largely depends, among others, on the way the data is represented; therefore, the choice how to represent the molecular structure information is extremely important. This article describes what the influence of the different representations on the clustering is and how it can be analyzed by means of a dendrogram comparison method. All experiments are performed using a data set consisting of RNA trinucleotides. Besides the most basic structure representation, the Cartesian coordinates representation, several other structure representations are used.     

Expert system for precision testing in validation of liquid chromatographic methods

After a liquid chromatographic method has been developed, it must be validated to establish its limitations in daily use. Method validation is becoming increasingly important as stricter rules are applied by regulatory authorities. Precision testing is a vital step in this validation; both intralaboratory testing and interlaboratory testing are needed. In an intralaboratory test, repeatability and ruggedness tests are usually done. Expert systems are available for both tests. Here they are integrated to form an intralaboratory precision-testing expert system; special integration architecture is described. Important features of the integrated system are a supervisor containing planning knowledge about the tests and a common data structure containing all the objects necessary for an expert system in this area.     

Expert systems for method development and validation in HPLC

ESCA, Expert Systems Applied to Chemical Analysis, started its research in March 1987, with the aim of building prototype expert systems for HPLC method development. Results of this research have been published as the work has progressed. The project is now completed and this paper summarises some of the overall project conclusions. Seven different expert systems have been built which tackle problems throughout the process of method development, four stand-alone systems and three integrated systems. The object of ESCA was to evaluate the applicability of expert system technology to analytical chemistry and not all the systems were built for commercial uses. Many of the systems tackle problems specific to one or more of the partners and thus may not be useful outside this environment. However, the results of the work are still pertinent to analysts wishing to build their own systems. These results are described, however, the emphasis of the paper is on those systems developed for method validation.Method validation for HPLC is a complex task which requires many characteristics of the method to be tested, e.g. accuracy, precision, etc. The expert systems built within ESCA concern the validation of precision. Two systems were developed for repeatability testing and ruggedness testing. The method validation process can be divided into several discrete stages, these include: (1) The selection of the method feature to test, for instance which factors can influence the ruggedness of a method. (2) The definition of a test procedure, for instance an efficient statistical design. (3) The execution of experiments and the interpretation of results. (4) A diagnosis of any observed problem. This paper describes these two systems in some detail and summarises some of the results obtained from their evaluation. It concludes that expert systems can be useful in solving analytical problems and the integration of several expert systems can provide extremely powerful tools for the analyst.     

Statistical methods for improving verification of claims of origin for Italian wines based on stable isotope ratios

Wine derives its economic value to a large extent from geographical origin, which has a significant impact on the quality of the wine. According to the food legislation, wines can be without geographical origin (table wine) and wines with origin. Wines with origin must have characteristics which are essential due to its region of production and must be produced, processed and prepared, exclusively within that region.     

The use of multivariate MR imaging intensities versus metabolic data from MR spectroscopic imaging for brain tumour classification

This study investigated the value of information from both magnetic resonance imaging and magnetic resonance spectroscopic imaging (MRSI) to automated discrimination of brain tumours. The influence of imaging intensities and metabolic data was tested by comparing the use of MR spectra from MRSI, MR imaging intensities, peak integration values obtained from the MR spectra and a combination of the latter two. Three classification techniques were objectively compared: linear discriminant analysis, least squares support vector machines (LS-SVM) with a linear kernel as linear techniques and LS-SVM with radial basis function kernel as a nonlinear technique. Classifiers were evaluated over 100 stratified random splittings of the dataset into training and test sets. The area under the receiver operating characteristic (ROC) curve (AUC) was used as a global performance measure on test data. In general, all techniques obtained a high performance when using peak integration values with or without MR imaging intensities. For example for low- versus high-grade tumours, low- versus high-grade gliomas and gliomas versus meningiomas, the mean test AUC was higher than 0.91, 0.94, and 0.99, respectively, when both MR imaging intensities and peak integration values were used. The use of metabolic data from MRSI significantly improved automated classification of brain tumour types compared to the use of MR imaging intensities solely.     

Improved DOSY NMR data processing by data enhancement and combination of multivariate curve resolution with non-linear least square fitting

The quality of DOSY NMR data can be improved by careful pre-processing techniques. Baseline drift, peak shift, and phase shift commonly exist in real-world DOSY NMR data. These phenomena seriously hinder the data analysis and should be removed as much as possible. In this paper, a series of preprocessing operations are proposed so that the subsequent multivariate curve resolution can yield optimal results. First, the baseline is corrected according to a method by Golotvin and Williams. Next, frequency and phase shift are removed by a new combination of reference deconvolution (FIDDLE), and a method presented by Witjes et al. that can correct several spectra simultaneously. The corrected data are analysed by the combination of multivariate curve resolution with non-linear least square regression (MCR-NLR). The MCR-NLR method turns out to be more robust and leads to better resolution of the pure components than classic MCR.     

Rapid crack growth in a thermoelastic solid under mixed-mode thermomechanical loading

A two-dimensional steady-sate analysis of semi-infinite brittlecrack growth at a constant subcritical rate in an unboundedfully-coupled thermoelastic solid under mixed-mode thermomechanicalloading is made. The loading consists of normal and shear tractionsand heat fluxes applied as point sources (line loads in theout-of-plane direction). A related problem is solved exactly in an integral transformspace, and robust asymptotic forms used to reduce the originalproblem to a set of integral equations. The equations are partiallycoupled and exhibit operators of both Cauchy and Abel types,yet can be solved analytically. The temperature change field at a distance from the moving crackedge is then constructed, and its dominant term is found tobe controlled by the imposed heat fluxes. The role of this termis, indeed, enhanced if the heat fluxes serve to render thecrack as a net heat source/sink for the solid, as opposed tobeing a transmitter of heat across its plane. More generally,the influence of the thermoelastic coupling on this field, aswell as other functions, is found to increase with crack speed.     

Untersuchung von Trinkwasser, Kesselspeisewasser, Abwasser

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