Reliability of a quantitative clinical posture assessment tool among persons with idiopathic scoliosis

Objective To determine overall, test–retest and inter-rater reliability of posture indices among persons with idiopathic scoliosis. Design A reliability study using two raters and two test sessions. Setting Tertiary care paediatric centre. Participants Seventy participants aged between 10 and 20 years with different types of idiopathic scoliosis (Cobb angle 15 to 60°) were recruited from the scoliosis clinic. Main outcome measures Based on the XY co-ordinates of natural reference points (e.g. eyes) as well as markers placed on several anatomical landmarks, 32 angular and linear posture indices taken from digital photographs in the standing position were calculated from a specially developed software program. Generalisability theory served to estimate the reliability and standard error of measurement (SEM) for the overall, test–retest and inter-rater designs. Bland and Altman's method was also used to document agreement between sessions and raters. Results In the random design, dependability coefficients demonstrated a moderate level of reliability for six posture indices (ϕ = 0.51 to 0.72) and a good level of reliability for 26 posture indices out of 32 (ϕ ≥ 0.79). Error attributable to marker placement was negligible for most indices. Limits of agreement and SEM values were larger for shoulder protraction, trunk list, Q angle, cervical lordosis and scoliosis angles. The most reproducible indices were waist angles and knee valgus and varus. Conclusions Posture can be assessed in a global fashion from photographs in persons with idiopathic scoliosis. Despite the good reliability of marker placement, other studies are needed to minimise measurement errors in order to provide a suitable tool for monitoring change in posture over time.

Underwater Target Localization, Tracking and Classification

Underwater target localization and tracking attracts tremendous research interest due to various impediments to the estimation task caused by the noisy ocean environment. This thesis envisages the implementation of a prototype automated system for underwater target localization, tracking and classification using passive listening buoy systems and target identification techniques. An autonomous three buoy system has been developed and field trials have been conducted successfully. Inaccuracies in the localization results, due to changes in the environmental parameters, measurement errors and theoretical approximations are refined using the Kalman filter approach. Simulation studies have been conducted for the tracking of targets with different scenarios even under maneuvering situations. This system can as well be used for classifying the unknown targets by extracting the features of the noise emanations from the targets.

Zur Erfassung von form- und materialbedingten Oberflächenstrukturen mit Mikro-Deflexions-Ellipsometrie

Die vorliegende Arbeit berichtet über ein neuartiges, kombiniertes Messverfahren zur gleichzeitigen Erfassung von Form und Material einer glänzenden Probenoberfläche. Die Materialerkennung erfolgt über die polarisationsoptische Brechzahlbestimmung im Messpunkt mit Mikroellipsometrie. Die Mikroellipsometrie ist ein fokussierendes Ellipsometer, das aus der Polarisationsänderung, bedingt durch die Wechselwirkung Licht – Materie, die materialcharakteristische komplexe Brechzahl eines reflektierenden Materials ermitteln kann. Bei der fokussierenden Ellipsometrie ist die Anordnung der fokussierenden Optiken von Bedeutung. Die hier vorgestellte ellipsometerexterne Fokussierung vermeidet Messfehler durch optische Anisotropien und ermöglicht die multispektrale ellipsometrische Messung. Für die ellipsometrische Brechzahlbestimmung ist zwingend die Kenntnis des Einfallswinkels des Messstrahls und die räumliche Orientierung der Oberflächenneigung zum Koordinatensystem des Ellipsometers notwendig. Die Oberflächenneigung wird mit einem Deflektometer ermittelt, das speziell für den Einsatz in Kombination mit der Ellipsometrie entwickelt wurde. Aus der lokalen Oberflächenneigung kann die Topographie einer Probe rekonstruiert werden. Der Einfallswinkel ist ebenfalls aus den Oberflächenneigungen ableitbar. Die Arbeit stellt die Systemtheorie der beiden kombinierten Messverfahren vor, außerdem werden Beiträge zu Messunsicherheiten diskutiert. Der experimentelle Teil der Arbeit beinhaltet die separate Untersuchung zur Leistungsfähigkeit der beiden zu kombinierenden Messverfahren. Die experimentellen Ergebnisse erlauben die Schlussfolgerung, dass ein Mikro-Deflexions-Ellipsometer erfolgreich realisierbar ist.

Femtosecond wavepacket interferometry using the rotational dynamics of a trapped cold molecular ion

A Ramsey-type interferometer is suggested, employing a cold trapped ion and two time-delayed offresonant femtosecond laser pulses. The laser light couples to the molecular polarization anisotropy, inducing rotational wavepacket dynamics. An interferogram is obtained from the delay dependent populations of the final field-free rotational states. Current experimental capabilities for cooling and preparation of the initial state are found to yield an interferogram visibility of more than 80%. The interferograms can be used to determine the polarizability anisotropy with an accuracy of about ±2%, respectively ±5%, provided the uncertainty in the initial populations and measurement errors are confined to within the same limits.

Clustering compositional data trajectories

Our essay aims at studying suitable statistical methods for the clustering of compositional data in situations where observations are constituted by trajectories of compositional data, that is, by sequences of composition measurements along a domain. Observed trajectories are known as “functional data” and several methods have been proposed for their analysis. In particular, methods for clustering functional data, known as Functional Cluster Analysis (FCA), have been applied by practitioners and scientists in many fields. To our knowledge, FCA techniques have not been extended to cope with the problem of clustering compositional data trajectories. In order to extend FCA techniques to the analysis of compositional data, FCA clustering techniques have to be adapted by using a suitable compositional algebra. The present work centres on the following question: given a sample of compositional data trajectories, how can we formulate a segmentation procedure giving homogeneous classes? To address this problem we follow the steps described below. First of all we adapt the well-known spline smoothing techniques in order to cope with the smoothing of compositional data trajectories. In fact, an observed curve can be thought of as the sum of a smooth part plus some noise due to measurement errors. Spline smoothing techniques are used to isolate the smooth part of the trajectory: clustering algorithms are then applied to these smooth curves. The second step consists in building suitable metrics for measuring the dissimilarity between trajectories: we propose a metric that accounts for difference in both shape and level, and a metric accounting for differences in shape only. A simulation study is performed in order to evaluate the proposed methodologies, using both hierarchical and partitional clustering algorithm. The quality of the obtained results is assessed by means of several indices

Condiciones de trabajo y morbilidad percibidas entre mineros de carbón en Guachetá, Colombia

Introducción: En Colombia la investigación sobre condiciones de trabajo y salud en minería carbonífera es escasa y no considera la percepción de la población expuesta y sus comportamientos frente a los riesgos inherentes. Objetivo: Determinar la asociación entre las condiciones de trabajo y morbilidad percibidas entre trabajadores de minas de carbón en Guachetá, Cundinamarca. Materiales y métodos: Se realizó un estudio transversal con 154 trabajadores seleccionados aleatoriamente del total registrado en la alcaldía municipal. Se indagó sobre características sociodemográficas, condiciones de trabajo y salud en las minas. Se estimaron prevalencias de los trastornos respiratorios, osteomusculares y auditivos, y se exploraron las asociaciones entre algunas condiciones de trabajo y los eventos con prevalencia superior a 30% de forma bivariada y múltiple, con regresiones Poisson con varianza robusta. Resultados: Los trabajadores fueron en su mayoría hombres, con edades entre 18 y 77 años de edad. Los problemas de salud más frecuentemente reportados fueron dolor lumbar (46,10%), dolor del miembro superior (40,26%), dolor del miembro inferior (34,42%), trastornos respiratorios (17,53%) y problemas auditivos (13,64%). Existen diferencias importantes en la percepción dependiendo de la antigüedad laboral y las condiciones subterráneas o no del trabajo. Conclusión: Los riesgos más reconocidos por los trabajadores son los relacionados con trastornos osteomusculares, al parecer por ser más evidentes en su cotidianidad. Las acciones en salud ocupacional podrán considerar estos hallazgos en sus planes de prevención de la enfermedad en las minas del carbón colombianas.

Technical instruments and conceptual definitions: theoretical and practical implications of the change on the definition of unemployment in Colombia

En junio de 2000 el Departamento Nacional de Estadística de Colombia adopto una nueva definición de medición de desempleo siguiendo los estándares sugeridos por la organización Internacional del Trabajo (OIT). El cambio de definición implico una reducción de la tasa de desempleo en cerca de dos puntos porcentuales. En este documento contrastamos la experiencia colombiana con otra experiencias internacionales, y analizamos las implicaciones empíricas y teóricas de este cambio de definición usando dos tipos de estimaciones cuantitativas: en la primera se contrasta las principales características de las diferentes categorías clasificadas según la definición nueva y vieja de desempleo (empleado, desempleado y fuera de la fuerza laboral) usando el algoritmo EM; en la segunda se pone a prueba la implicación del desempleo estructural y su relación con el perfil educacional de personas desempleadas y las características teóricas que enfrentan los estándares de la OIT en la definición de empleo.

Automatic internal calibration in liquid chromatography/Fourier transform ion cyclotron resonance mass spectrometry of protein digests

Accurately measured peptide masses can be used for large-scale protein identification from bacterial whole-cell digests as an alternative to tandem mass spectrometry (MS/MS) provided mass measurement errors of a few parts-per-million (ppm) are obtained. Fourier transform ion cyclotron resonance (FTICR) mass spectrometry (MS) routinely achieves such mass accuracy either with internal calibration or by regulating the charge in the analyzer cell. We have developed a novel and automated method for internal calibration of liquid chromatography (LC)/FTICR data from whole-cell digests using peptides in the sample identified by concurrent MS/MS together with ambient polydimethyl-cyclosiloxanes as internal calibrants in the mass spectra. The method reduced mass measurement error from 4.3 +/- 3.7 ppm to 0.3 +/- 2.3 ppm in an E. coli LC/FTICR dataset of 1000 MS and MS/MS spectra and is applicable to all analyses of complex protein digests by FTICRMS. Copyright (c) 2006 John Wiley & Sons, Ltd.

Enhancing model predictive control using dynamic data reconciliation

The use of data reconciliation techniques can considerably reduce the inaccuracy of process data due to measurement errors. This in turn results in improved control system performance and process knowledge. Dynamic data reconciliation techniques are applied to a model-based predictive control scheme. It is shown through simulations on a chemical reactor system that the overall performance of the model-based predictive controller is enhanced considerably when data reconciliation is applied. The dynamic data reconciliation techniques used include a combined strategy for the simultaneous identification of outliers and systematic bias.

Validation of ACE-FTS satellite data in the upper troposphere/lower stratosphere (UTLS) using non-coincident measurements

CO, O3, and H2O data in the upper troposphere/lower stratosphere (UTLS) measured by the Atmospheric Chemistry Experiment Fourier Transform Spectrometer(ACE-FTS) on Canada’s SCISAT-1 satellite are validated using aircraft and ozonesonde measurements. In the UTLS, validation of chemical trace gas measurements is a challenging task due to small-scale variability in the tracer fields, strong gradients of the tracers across the tropopause, and scarcity of measurements suitable for validation purposes. Validation based on coincidences therefore suffers from geophysical noise. Two alternative methods for the validation of satellite data are introduced, which avoid the usual need for coincident measurements: tracer-tracer correlations, and vertical tracer profiles relative to tropopause height. Both are increasingly being used for model validation as they strongly suppress geophysical variability and thereby provide an “instantaneous climatology”. This allows comparison of measurements between non-coincident data sets which yields information about the precision and a statistically meaningful error-assessment of the ACE-FTS satellite data in the UTLS. By defining a trade-off factor, we show that the measurement errors can be reduced by including more measurements obtained over a wider longitude range into the comparison, despite the increased geophysical variability. Applying the methods then yields the following upper bounds to the relative differences in the mean found between the ACE-FTS and SPURT aircraft measurements in the upper troposphere (UT) and lower stratosphere (LS), respectively: for CO ±9% and ±12%, for H2O ±30% and ±18%, and for O3 ±25% and ±19%. The relative differences for O3 can be narrowed down by using a larger dataset obtained from ozonesondes, yielding a high bias in the ACEFTS measurements of 18% in the UT and relative differences of ±8% for measurements in the LS. When taking into account the smearing effect of the vertically limited spacing between measurements of the ACE-FTS instrument, the relative differences decrease by 5–15% around the tropopause, suggesting a vertical resolution of the ACE-FTS in the UTLS of around 1 km. The ACE-FTS hence offers unprecedented precision and vertical resolution for a satellite instrument, which will allow a new global perspective on UTLS tracer distributions.

Intergenerational wealth mobility in rural Bangladesh

Unique residential history data with retrospective information on parental assets are used to study household wealth mobility in 141 villages in rural Bangladesh. Regression estimates of father–son correlations and analyses of intergenerational transition matrices show substantial persistence in wealth even when we correct for measurement errors in parental wealth. We do not find wealth mobility to be higher between periods of a person's life than between generations. We find that the process of household division plays an important role: sons who splinter off from the father's household experience greater (albeit downward) mobility in wealth. Despite significant occupational mobility across generations, its contribution to wealth mobility, net of human capital attainment of individuals, appears insignificant. Low wealth mobility in our data is primarily explained by intergenerational persistence in educational attainment.

Robust approaches to forecasting

We investigate alternative robust approaches to forecasting, using a new class of robust devices, contrasted with equilibrium-correction models. Their forecasting properties are derived facing a range of likely empirical problems at the forecast origin, including measurement errors, impulses, omitted variables, unanticipated location shifts and incorrectly included variables that experience a shift. We derive the resulting forecast biases and error variances, and indicate when the methods are likely to perform well. The robust methods are applied to forecasting US GDP using autoregressive models, and also to autoregressive models with factors extracted from a large dataset of macroeconomic variables. We consider forecasting performance over the Great Recession, and over an earlier more quiescent period.

Heteroscedastic controlled calibration model applied to analytical chemistry

In chemical analyses performed by laboratories, one faces the problem of determining the concentration of a chemical element in a sample. In practice, one deals with the problem using the so-called linear calibration model, which considers that the errors associated with the independent variables are negligible compared with the former variable. In this work, a new linear calibration model is proposed assuming that the independent variables are subject to heteroscedastic measurement errors. A simulation study is carried out in order to verify some properties of the estimators derived for the new model and it is also considered the usual calibration model to compare it with the new approach. Three applications are considered to verify the performance of the new approach. Copyright (C) 2010 John Wiley & Sons, Ltd.

Improved maximum-likelihood estimation in a regression model with general parametrization

We analyse the finite-sample behaviour of two second-order bias-corrected alternatives to the maximum-likelihood estimator of the parameters in a multivariate normal regression model with general parametrization proposed by Patriota and Lemonte [A. G. Patriota and A. J. Lemonte, Bias correction in a multivariate regression model with genereal parameterization, Stat. Prob. Lett. 79 (2009), pp. 1655-1662]. The two finite-sample corrections we consider are the conventional second-order bias-corrected estimator and the bootstrap bias correction. We present the numerical results comparing the performance of these estimators. Our results reveal that analytical bias correction outperforms numerical bias corrections obtained from bootstrapping schemes.

Bias correction in a multivariate normal regression model with general parameterization

This paper derives the second-order biases Of maximum likelihood estimates from a multivariate normal model where the mean vector and the covariance matrix have parameters in common. We show that the second order bias can always be obtained by means of ordinary weighted least-squares regressions. We conduct simulation studies which indicate that the bias correction scheme yields nearly unbiased estimators. (C) 2009 Elsevier B.V. All rights reserved.