Participation in leisure and physical activity among children and adolescents with juvenile idiopathic arthritis

Contexte: L’arthrite juvénile idiopathique (AJI) est l’une des maladies chroniques auto-immune les plus répandues chez les enfants et est caractérisée par des enflures articulaires (maladie active), de la douleur, de la fatigue et des raideurs matinales pouvant restreindre leur niveau de participation aux activités quotidiennes (par exemple: les loisirs, l’activité physique, la mobilité et les soins personnels) à la maison comme à l’école. Participer aux activités de loisirs et à l’activité physique a des bienfaits au niveau de la santé et du développement de tous les enfants et démontrent aussi des effets positifs qui réduisent les symptômes des maladies chroniques telle l’AJI. Malgré ces bienfaits la participation aux loisirs chez les jeunes avec l’AJI demeure largement sous-étudiée. Objectifs: Cette étude vise à évaluer le niveau de participation aux loisirs et à l’activité physique chez les enfants et les adolescents atteints d’AJI, ainsi qu’à identifier les facteurs liés à la maladie, la personne et l’environnement. Méthodes : L’évaluation du niveau de participation et l’exploration des facteurs associés aux loisirs et à l’activité physique ont été complétés par l’entremise d’une revue systématique de la littérature, l’analyse de données d’un échantillon national représentatif d’enfants canadiens atteints d’arthrite âgés entre 5 et 14 ans (npondéré = 4350), ainsi que l’analyse standardisée du niveau de participation aux loisirs à l’aide du Children’s Assessment of Participation and Enjoyment (n=107) et la mesure objective de l’activité physique par accéléromètre (n=76) auprès d’un échantillon d’enfants (âgés entre 8 et 11 ans ) et d’adolescents (âgés entre 12 et 17 ans) suivis en clinique de rhumatologie à l’hôpital de Montréal pour enfants, Centre Universitaire de Santé McGill. Les résultats cliniques ont été comparés à des données normatives, ainsi qu’à un groupe contrôle sans AJI. Nous avons exploré les facteurs associés avec le niveau de participation aux loisirs et à l’activité physique en utilisant les modèles de régression linéaire multiple et l’analyse hiérarchique. Résultats : Les enfants et les adolescents atteints d’AJI participent à une multitude d’activités de loisirs; cependant ils sont moins souvent impliqués dans des activités physiques et de raffinement en comparaison aux autres types d’activités de loisirs. Ceux avec l’AJI étaient en général moins actifs que leurs pairs sans arthrite et la plupart n’atteignaient pas les recommandations nationales d’activité physique. Les garçons avec l’AJI participent plus souvent à des activités physiques et moins aux activités sociales, de raffinement et de développement de soi en comparaison avec les filles ayant l’AJI. En général, être un garçon, être plus âgé, avoir une meilleure motivation pour participer aux activités de motricité globale, avoir un statut socio-économique plus élevé et être d’origine culturelle canadienne sont associés à un niveau de participation plus élevé aux activités physiques. La préférence pour les activités de raffinement, un niveau d’éducation maternelle plus élevé et être une fille étaient associés à un niveau de participation plus élevé aux activités de raffinement. Conclusion: La participation aux loisirs et à l’activité physique en AJI est un concept complexe et semble surtout être expliqué par des facteurs personnels et environnementaux. L’identification des facteurs associés aux loisirs et à l’activité physique est très importante en AJI puisqu’elle peut permettre aux professionnels de la santé de développer des interventions significatives basées sur les activités préférées des enfants, améliorer l’observance au traitement et promouvoir des habitudes de vie saine.

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.

Modified Large Margin Nearest Neighbor Metric Learning for Regression

The main objective of this letter is to formulate a new approach of learning a Mahalanobis distance metric for nearest neighbor regression from a training sample set. We propose a modified version of the large margin nearest neighbor metric learning method to deal with regression problems. As an application, the prediction of post-operative trunk 3-D shapes in scoliosis surgery using nearest neighbor regression is described. Accuracy of the proposed method is quantitatively evaluated through experiments on real medical data.

Statistical model based 3D shape prediction of postoperative trunks for non-invasive scoliosis surgery planning

One of the major concerns of scoliosis patients undergoing surgical treatment is the aesthetic aspect of the surgery outcome. It would be useful to predict the postoperative appearance of the patient trunk in the course of a surgery planning process in order to take into account the expectations of the patient. In this paper, we propose to use least squares support vector regression for the prediction of the postoperative trunk 3D shape after spine surgery for adolescent idiopathic scoliosis. Five dimensionality reduction techniques used in conjunction with the support vector machine are compared. The methods are evaluated in terms of their accuracy, based on the leave-one-out cross-validation performed on a database of 141 cases. The results indicate that the 3D shape predictions using a dimensionality reduction obtained by simultaneous decomposition of the predictors and response variables have the best accuracy.

Wave-vector dependence of spin and density multipole excitations in quantum dots

We have employed time-dependent local-spin density-functional theory to analyze the multipole spin and charge density excitations in GaAs-AlxGa1-xAs quantum dots. The on-plane transferred momentum degree of freedom has been taken into account, and the wave-vector dependence of the excitations is discussed. In agreement with previous experiments, we have found that the energies of these modes do not depend on the transferred wave vector, although their intensities do. Comparison with a recent resonant Raman scattering experiment [C. Schüller et al., Phys. Rev. Lett. 80, 2673 (1998)] is made. This allows us to identify the angular momentum of several of the observed modes as well as to reproduce their energies

Studies on the meteriological aspects of air pollution over Cochin

Pollutants that once enter into the earth’s atmosphere become part of the atmosphere and hence their dispersion, dilution, direction of transportation etc. are governed by the meteorological conditions. The thesis deals with the study of the atmospheric dispersion capacity, wind climatology, atmospheric stability, pollutant distribution by means of a model and the suggestions for a comprehensive planning for the industrially developing city, Cochin. The definition, sources, types and effects of air pollution have been dealt with briefly. The influence of various meteorological parameters such as vector wind, temperature and its vertical structure and atmospheric stability in relation to pollutant dispersal have been studied. The importance of inversions, mixing heights, ventilation coefficients were brought out. The spatial variation of mixing heights studies for the first time on a microscale region, serves to delineate the regions of good and poor dispersal capacity. A study of wind direction fluctuation, σθ and its relation to stability and mixing heights were shown to be much useful. It was shown that there is a necessity to look into the method of σθ computation. The development of Gausssian Plume Model along with the application for multiple sources was presented. The pollutant chosen was sulphur dioxide and industrial sources alone were considered. The percentage frequency of occurrence of inversions and isothermals are found to be low in all months during the year. The spatial variation of mixing heights revealed that a single mixing height cannot be taken as a representative for the whole city have low mixing heights and monsoonal months showed lowest mixing heights. The study of ventilation co-efficients showed values less than the required optimum value 6000m2/5. However, the low values may be due to the consideration of surface wind alone instead of the vertically averaged wind. Relatively more calm conditions and light winds during night and strong winds during day time were observed. During the most of the year westerlies during day time and northeasterlies during night time are the dominant winds. Unstable conditions with high values of σθ during day time and stable conditions with lower values of σθ during night time are the prominent features. Monsoonal months showed neutral stability for most of the time. A study σθ of and Pasquill Stability category has revealed the difficulty in giving a unique value of for each stability category. For the first time regression equations have been developed relating mixing heights and σθ. A closer examination of σθ revealed that half of the range of wind direction fluctuations is to be taken, instead of one by sixth, to compute σθ. The spatial distribution of SO2 showed a more or less uniform distribution with a slight intrusion towards south. Winter months showed low concentrations contrary to the expectations. The variations of the concentration is found to be influenced more by the mixing height and the stack height rather than wind speed. In the densely populated areas the concentration is more than the threshold limit value. However, the values reported appear to be high, because no depletion of the material is assumed through dry or wet depositions and also because of the inclusion of calm conditions with a very light wind speed. A reduction of emission during night time with a consequent rise during day time would bring down the levels of pollution. The probable locations for the new industries could be the extreme southeast parts because the concentration towards the north falls off very quickly resulting low concentrations. In such a case pollutant spread would be towards south and west, thus keeping the city interior relatively free from pollution. A more detailed examination of the pollutant spread by means of models that would take the dry and wet depositions may be necessary. Nevertheless, the present model serves to give the trend of the distribution of pollutant concentration with which one can suggest the optimum locations for the new industries

An Improved Color Video Super-Resolution Using Kernel Regression and Fuzzy Enhancement

An improved color video super-resolution technique using kernel regression and fuzzy enhancement is presented in this paper. A high resolution frame is computed from a set of low resolution video frames by kernel regression using an adaptive Gaussian kernel. A fuzzy smoothing filter is proposed to enhance the regression output. The proposed technique is a low cost software solution to resolution enhancement of color video in multimedia applications. The performance of the proposed technique is evaluated using several color videos and it is found to be better than other techniques in producing high quality high resolution color videos

Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective

Bayes quadratic unbiased estimator of spatial covariance parameters

This work presents Bayes invariant quadratic unbiased estimator, for short BAIQUE. Bayesian approach is used here to estimate the covariance functions of the regionalized variables which appear in the spatial covariance structure in mixed linear model. Firstly a brief review of spatial process, variance covariance components structure and Bayesian inference is given, since this project deals with these concepts. Then the linear equations model corresponding to BAIQUE in the general case is formulated. That Bayes estimator of variance components with too many unknown parameters is complicated to be solved analytically. Hence, in order to facilitate the handling with this system, BAIQUE of spatial covariance model with two parameters is considered. Bayesian estimation arises as a solution of a linear equations system which requires the linearity of the covariance functions in the parameters. Here the availability of prior information on the parameters is assumed. This information includes apriori distribution functions which enable to find the first and the second moments matrix. The Bayesian estimation suggested here depends only on the second moment of the prior distribution. The estimation appears as a quadratic form y'Ay , where y is the vector of filtered data observations. This quadratic estimator is used to estimate the linear function of unknown variance components. The matrix A of BAIQUE plays an important role. If such a symmetrical matrix exists, then Bayes risk becomes minimal and the unbiasedness conditions are fulfilled. Therefore, the symmetry of this matrix is elaborated in this work. Through dealing with the infinite series of matrices, a representation of the matrix A is obtained which shows the symmetry of A. In this context, the largest singular value of the decomposed matrix of the infinite series is considered to deal with the convergence condition and also it is connected with Gerschgorin Discs and Poincare theorem. Then the BAIQUE model for some experimental designs is computed and compared. The comparison deals with different aspects, such as the influence of the position of the design points in a fixed interval. The designs that are considered are those with their points distributed in the interval [0, 1]. These experimental structures are compared with respect to the Bayes risk and norms of the matrices corresponding to distances, covariance structures and matrices which have to satisfy the convergence condition. Also different types of the regression functions and distance measurements are handled. The influence of scaling on the design points is studied, moreover, the influence of the covariance structure on the best design is investigated and different covariance structures are considered. Finally, BAIQUE is applied for real data. The corresponding outcomes are compared with the results of other methods for the same data. Thereby, the special BAIQUE, which estimates the general variance of the data, achieves a very close result to the classical empirical variance.

Dress- and Auto- Makers in the “Free Trade” Arena

Even though there have been many studies on the impact of trade liberalisation on labour standards, most of the studies are at national level, and there is a lack of research at industry level. This paper examines the impact of free trade on labour standards in capital- and labour-intensive industries in a developing country. For empirical findings, I take the case of the garment industry, representing labour-intensive industry, and automotive industry, representing capital-intensive industry, in Indonesia in the face of ASEAN Free Trade Area (AFTA). Since the garment industry is a women-dominated industry, while the automotive industry is a men-dominated industry, this paper also employs a feminist perspective. As such, this paper also investigates whether free trade equally affects men and women workers. Besides free trade, other independent variables are also taken into account. Employing quantitative and qualitative methods, empirical evidence shows that there is an indication that free trade has a negative relationship with labour standards in the garment industry, whereas a positive relationships with labour standards in the automotive industry. This implies that free trade might result in decreasing labour standards in labour-intensive industry, while increasing standards in capital-intensive industry. It can also be inferred that free trade unequally affect men and women workers, in that women workers bear the brunt of free trade. The results also show that other internal and external independent variables are indicated to have relationships with labour standards in the garment and automotive industries. Therefore, these variables need to be considered in examining the extent of the impact of free trade on labour standards in labour- and capital-intensive industries.

A Vector Signal Processing Approach to Color

Surface (Lambertain) color is a useful visual cue for analyzing material composition of scenes. This thesis adopts a signal processing approach to color vision. It represents color images as fields of 3D vectors, from which we extract region and boundary information. The first problem we face is one of secondary imaging effects that makes image color different from surface color. We demonstrate a simple but effective polarization based technique that corrects for these effects. We then propose a systematic approach of scalarizing color, that allows us to augment classical image processing tools and concepts for multi-dimensional color signals.

Learning-Based Approach to Real Time Tracking and Analysis of Faces

This paper describes a trainable system capable of tracking faces and facialsfeatures like eyes and nostrils and estimating basic mouth features such as sdegrees of openness and smile in real time. In developing this system, we have addressed the twin issues of image representation and algorithms for learning. We have used the invariance properties of image representations based on Haar wavelets to robustly capture various facial features. Similarly, unlike previous approaches this system is entirely trained using examples and does not rely on a priori (hand-crafted) models of facial features based on optical flow or facial musculature. The system works in several stages that begin with face detection, followed by localization of facial features and estimation of mouth parameters. Each of these stages is formulated as a problem in supervised learning from examples. We apply the new and robust technique of support vector machines (SVM) for classification in the stage of skin segmentation, face detection and eye detection. Estimation of mouth parameters is modeled as a regression from a sparse subset of coefficients (basis functions) of an overcomplete dictionary of Haar wavelets.

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.