Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design

A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

An Exponentially Convergent Nonpolynomial Finite Element Method for Time-Harmonic Scattering from Polygons

In recent years nonpolynomial finite element methods have received increasing attention for the efficient solution of wave problems. As with their close cousin the method of particular solutions, high efficiency comes from using solutions to the Helmholtz equation as basis functions. We present and analyze such a method for the scattering of two-dimensional scalar waves from a polygonal domain that achieves exponential convergence purely by increasing the number of basis functions in each element. Key ingredients are the use of basis functions that capture the singularities at corners and the representation of the scattered field towards infinity by a combination of fundamental solutions. The solution is obtained by minimizing a least-squares functional, which we discretize in such a way that a matrix least-squares problem is obtained. We give computable exponential bounds on the rate of convergence of the least-squares functional that are in very good agreement with the observed numerical convergence. Challenging numerical examples, including a nonconvex polygon with several corner singularities, and a cavity domain, are solved to around 10 digits of accuracy with a few seconds of CPU time. The examples are implemented concisely with MPSpack, a MATLAB toolbox for wave computations with nonpolynomial basis functions, developed by the authors. A code example is included.

DETERMINATION of SILICON IN LUBRICANT OIL BY HIGH-RESOLUTION CONTINUUM SOURCE FLAME ATOMIC ABSORPTION SPECTROMETRY USING LEAST-SQUARE BACKGROUND CORRECTION and INTERNAL STANDARDIZATION

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multivariate quality control studies applied to Ca(II) and Mg(II) determination by a portable method

A portable or field test method for simultaneous spectrophotometric determination of calcium and magnesium in water using multivariate partial least squares (PLS) calibration methods is proposed. The method is based on the reaction between the analytes and methylthymol blue at pH 11. The spectral information was used as the X-block, and the Ca(II) and Mg(II) concentrations obtained by a reference technique (ICP-AES) were used as the Y-block. Two series of analyses were performed, with a month's difference between them. The first series was used as the calibration set and the second one as the validation set. Multivariate statistical process control (MSPC) techniques, based on statistics from principal component models, were used to study the features and evolution with time of the spectral signals. Signal standardization was used to correct the deviations between series. Method validation was performed by comparing the predictions of the PLS model with the reference Ca(II) and Mg(II) concentrations determined by ICP-AES using the joint interval test for the slope and intercept of the regression line with errors in both axes. (C) 1998 John Wiley & Sons, Ltd.

The STATFLUX code: a statistical method for calculation of flow and set of parameters, based on the Multiple-Compartment Biokinetical Model

The code STATFLUX, implementing a new and simple statistical procedure for the calculation of transfer coefficients in radionuclide transport to animals and plants, is proposed. The method is based on the general multiple-compartment model, which uses a system of linear equations involving geometrical volume considerations. Flow parameters were estimated by employing two different least-squares procedures: Derivative and Gauss-Marquardt methods, with the available experimental data of radionuclide concentrations as the input functions of time. The solution of the inverse problem, which relates a given set of flow parameter with the time evolution of concentration functions, is achieved via a Monte Carlo Simulation procedure.Program summaryTitle of program: STATFLUXCatalogue identifier: ADYS_v1_0Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADYS_v1_0Program obtainable from: CPC Program Library, Queen's University of Belfast, N. IrelandLicensing provisions: noneComputer for which the program is designed and others on which it has been tested: Micro-computer with Intel Pentium III, 3.0 GHzInstallation: Laboratory of Linear Accelerator, Department of Experimental Physics, University of São Paulo, BrazilOperating system: Windows 2000 and Windows XPProgramming language used: Fortran-77 as implemented in Microsoft Fortran 4.0. NOTE: Microsoft Fortran includes non-standard features which are used in this program. Standard Fortran compilers such as, g77, f77, ifort and NAG95, are not able to compile the code and therefore it has not been possible for the CPC Program Library to test the program.Memory, required to execute with typical data: 8 Mbytes of RAM memory and 100 MB of Hard disk memoryNo. of bits in a word: 16No. of lines in distributed program, including test data, etc.: 6912No. of bytes in distributed Program, including test data, etc.: 229 541Distribution format: tar.gzNature of the physical problem: the investigation of transport mechanisms for radioactive substances, through environmental pathways, is very important for radiological protection of populations. One such pathway, associated with the food chain, is the grass-animal-man sequence. The distribution of trace elements in humans and laboratory animals has been intensively studied over the past 60 years [R.C. Pendlenton, C.W. Mays, R.D. Lloyd, A.L. Brooks, Differential accumulation of iodine-131 from local fallout in people and milk, Health Phys. 9 (1963) 1253-1262]. In addition, investigations on the incidence of cancer in humans, and a possible causal relationship to radioactive fallout, have been undertaken [E.S. Weiss, M.L. Rallison, W.T. London, W.T. Carlyle Thompson, Thyroid nodularity in southwestern Utah school children exposed to fallout radiation, Amer. J. Public Health 61 (1971) 241-249; M.L. Rallison, B.M. Dobyns, F.R. Keating, J.E. Rall, F.H. Tyler, Thyroid diseases in children, Amer. J. Med. 56 (1974) 457-463; J.L. Lyon, M.R. Klauber, J.W. Gardner, K.S. Udall, Childhood leukemia associated with fallout from nuclear testing, N. Engl. J. Med. 300 (1979) 397-402]. From the pathways of entry of radionuclides in the human (or animal) body, ingestion is the most important because it is closely related to life-long alimentary (or dietary) habits. Those radionuclides which are able to enter the living cells by either metabolic or other processes give rise to localized doses which can be very high. The evaluation of these internally localized doses is of paramount importance for the assessment of radiobiological risks and radiological protection. The time behavior of trace concentration in organs is the principal input for prediction of internal doses after acute or chronic exposure. The General Multiple-Compartment Model (GMCM) is the powerful and more accepted method for biokinetical studies, which allows the calculation of concentration of trace elements in organs as a function of time, when the flow parameters of the model are known. However, few biokinetics data exist in the literature, and the determination of flow and transfer parameters by statistical fitting for each system is an open problem.Restriction on the complexity of the problem: This version of the code works with the constant volume approximation, which is valid for many situations where the biological half-live of a trace is lower than the volume rise time. Another restriction is related to the central flux model. The model considered in the code assumes that exist one central compartment (e.g., blood), that connect the flow with all compartments, and the flow between other compartments is not included.Typical running time: Depends on the choice for calculations. Using the Derivative Method the time is very short (a few minutes) for any number of compartments considered. When the Gauss-Marquardt iterative method is used the calculation time can be approximately 5-6 hours when similar to 15 compartments are considered. (C) 2006 Elsevier B.V. All rights reserved.

A combined wavelet-element free Galerkin method for numerical calculations of electromagnetic fields

A combined wavelet-element free Galerkin (EFG) method is proposed for solving electromagnetic EM) field problems. The bridging scales are used to preserve the consistency and linear independence properties of the entire bases. A detailed description of the development of the discrete model and its numerical implementations is given to facilitate the reader to. understand the proposed algorithm. A numerical example to validate the proposed method is also reported.

An effective wavelet method to detect and mitigate low-frequency multipath effects

To ensure high accuracy results from GPS relative positioning, the multipath effects have to be mitigated. Although the careful selection of antenna site and the use of especial antennas and receivers can minimize multipath, it cannot always be eliminated and frequently the residual multipath disturbance remains as the major error in GPS results. The high-frequency multipath from large delays can be attenuated by double difference (DD) denoising methods. But the low-frequency multipath from short delays is very difficult to be reduced or modeled. In this paper, it is proposed a method based on wavelet regression (WR), which can effectively detect and reduce the low-frequency multipath. The wavelet technique is firstly applied to decompose the DD residuals into the low-frequency bias and high-frequency noise components. The extracted bias components by WR are then directly applied to the DD observations to correct them from the trend. The remaining terms, largely characterized by the high-frequency measurement noise, are expected to give the best linear unbiased solutions from a least-squares (LS) adjustment. An experiment was carried out using objects placed close to the receiver antenna to cause, mainly, low-frequency multipath. The data were collected for two days to verify the multipath repeatability. The ground truth coordinates were computed with data collected in the absence of the reflector objects. The coordinates and ambiguity solution were compared with and without the multipath mitigation using WR. After mitigating the multipath, ambiguity resolution became more reliable and the coordinates were more accurate.

Development and validation of an environmentally friendly attenuated total reflectance in the mid-infrared region method for the determination of ethanol content in used engine lubrication oil

Lubricating oils are crucial in the operation of automotive engines because they both reduce friction between moving parts and protect against corrosion. However, the performance of lubricant oil may be affected by contaminants, such as gasoline, diesel, ethanol, water and ethylene glycol. Although there are many standard methods and studies related to the quantification of contaminants in lubricant oil, such as gasoline and diesel oil, to the best of our knowledge, no methods have been reported for the quantification of ethanol in used Otto cycle engine lubrication oils. Therefore, this work aimed at the development and validation of a routine method based on partial least-squares multivariate analysis combined with attenuated total reflectance in the mid-infrared region to quantify ethanol content in used lubrication oil. The method was validated based on its figures of merit (using the net analyte signal) as follows: limit of detection (0.049%), limit of quantification (0.16%), accuracy (root mean square error of prediction=0.089% w/w), repeatability (0.05% w/w), fit (R 2 =0.9997), mean selectivity (0.047), sensitivity (0.011), inverse analytical sensitivity (0.016% w/w-1) and signal-to-noise ratio (max: 812.4 and min: 200.9). The results show that the proposed method can be routinely implemented for the quality control of lubricant oils. © 2013 Elsevier B.V. All rights reserved.

Inversion method for obtaining electrical parameters for soil in the rural region in Brazil

This paper presents an inversion methodology through weighted least squares to obtain the electrical parameters for the soil of a typical mid-western region in Brazil using the model based in the formalism of the parabolic equations to calculate the electric field intensity received. To validate this methodology, the results of the radio signal measurement campaign conducted in six radial routes leaving the city of Brasilia, Federal District, where the transmitter was located, were used. The measurements were compared to computer simulations and, thus, the optimal values for the electric conductivity and relative permittivity for the soil of the region could be estimated. Finally, a quantitative analysis of these parameters was performed with the values found in the literature, which demonstrated the effectiveness of the proposed methodology.

Method validation using weighted linear regression models for quantification of UV filters in water samples

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Gaussian deconvolution: a useful method for a form-free modeling of scattering data from mono- and multilayered planar systems

A new method for analysis of scattering data from lamellar bilayer systems is presented. The method employs a form-free description of the cross-section structure of the bilayer and the fit is performed directly to the scattering data, introducing also a structure factor when required. The cross-section structure (electron density profile in the case of X-ray scattering) is described by a set of Gaussian functions and the technique is termed Gaussian deconvolution. The coefficients of the Gaussians are optimized using a constrained least-squares routine that induces smoothness of the electron density profile. The optimization is coupled with the point-of-inflection method for determining the optimal weight of the smoothness. With the new approach, it is possible to optimize simultaneously the form factor, structure factor and several other parameters in the model. The applicability of this method is demonstrated by using it in a study of a multilamellar system composed of lecithin bilayers, where the form factor and structure factor are obtained simultaneously, and the obtained results provided new insight into this very well known system.

New regularization method for EXAFS analysis: Application to uranium and plutonium sorption onto kaolinite

Die Röntgenabsorptionsspektroskopie (Extended X-ray absorption fine structure (EXAFS) spectroscopy) ist eine wichtige Methode zur Speziation von Schwermetallen in einem weiten Bereich von umweltrelevanten Systemen. Um Strukturparameter wie Koordinationszahl, Atomabstand und Debye-Waller Faktoren für die nächsten Nachbarn eines absorbierenden Atoms zu bestimmen, ist es für experimentelle EXAFS-Spektren üblich, unter Verwendung von Modellstrukturen einen „Least-Squares-Fit“ durchzuführen. Oft können verschiedene Modellstrukturen mit völlig unterschiedlicher chemischer Bedeutung die experimentellen EXAFS-Daten gleich gut beschreiben. Als gute Alternative zum konventionellen Kurven-Fit bietet sich das modifizierte Tikhonov-Regularisationsverfahren an. Ergänzend zur Tikhonov-Standardvariationsmethode enthält der in dieser Arbeit vorgestellte Algorithmus zwei weitere Schritte, nämlich die Anwendung des „Method of Separating Functionals“ und ein Iterationsverfahren mit Filtration im realen Raum. Um das modifizierte Tikhonov-Regularisationsverfahren zu testen und zu bestätigen wurden sowohl simulierte als auch experimentell gemessene EXAFS-Spektren einer kristallinen U(VI)-Verbindung mit bekannter Struktur, nämlich Soddyit (UO2)2SiO4 x 2H2O, untersucht. Die Leistungsfähigkeit dieser neuen Methode zur Auswertung von EXAFS-Spektren wird durch ihre Anwendung auf die Analyse von Proben mit unbekannter Struktur gezeigt, wie sie bei der Sorption von U(VI) bzw. von Pu(III)/Pu(IV) an Kaolinit auftreten. Ziel der Dissertation war es, die immer noch nicht voll ausgeschöpften Möglichkeiten des modifizierten Tikhonov-Regularisationsverfahrens für die Auswertung von EXAFS-Spektren aufzuzeigen. Die Ergebnisse lassen sich in zwei Kategorien einteilen. Die erste beinhaltet die Entwicklung des Tikhonov-Regularisationsverfahrens für die Analyse von EXAFS-Spektren von Mehrkomponentensystemen, insbesondere die Wahl bestimmter Regularisationsparameter und den Einfluss von Mehrfachstreuung, experimentell bedingtem Rauschen, etc. auf die Strukturparameter. Der zweite Teil beinhaltet die Speziation von sorbiertem U(VI) und Pu(III)/Pu(IV) an Kaolinit, basierend auf experimentellen EXAFS-Spektren, die mit Hilfe des modifizierten Tikhonov-Regularisationsverfahren ausgewertet und mit Hilfe konventioneller EXAFS-Analyse durch „Least-Squares-Fit“ bestätigt wurden.

Approximating method of frames

In this paper, the well-known method of frames approach to the signal decomposition problem is reformulated as a certain bilevel goal-attainment linear least squares problem. As a consequence, a numerically robust variant of the method, named approximating method of frames, is proposed on the basis of a certain minimal Euclidean norm approximating splitting pseudo-iteration-wise method.

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^

Bernstein polynomials in element-free Galerkin method

In the recent decades, meshless methods (MMs), like the element-free Galerkin method (EFGM), have been widely studied and interesting results have been reached when solving partial differential equations. However, such solutions show a problem around boundary conditions, where the accuracy is not adequately achieved. This is caused by the use of moving least squares or residual kernel particle method methods to obtain the shape functions needed in MM, since such methods are good enough in the inner of the integration domains, but not so accurate in boundaries. This way, Bernstein curves, which are a partition of unity themselves,can solve this problem with the same accuracy in the inner area of the domain and at their boundaries.