Exploring food choice as social practice : appreciating the context of family feeding in Kahnawake, Québec, Canada

De nous jours, les modèles se référant aux comportements individuels représentent la pensée dominante pour comprendre les choix alimentaires dans le domaine de la nutrition en santé publique. Ces modèles conceptualisent les choix alimentaires comme un comportement de consommation décidé de façon rationnelle par des individus, en réponse aux multiples déterminants personnels et environnementaux. Même si ces modèles sont utiles pour décrire les déterminants des comportements individuels d’alimentation, ils ne peuvent expliquer les choix alimentaires en tant que processus social façonné en fonction des individus et des lieux, dans des contextes diversifiés. Cette thèse élabore le Cadre Conceptuel sur la Pratique des Choix Alimentaires afin d’explorer les choix alimentaires comme phénomène social. En utilisant le concept de pratique sociale, les choix alimentaires des individus symbolisent une relation récursive entre la structure sociale et l’agence. Ce cadre conceptuel nous donne un moyen d’identifier les choix alimentaires comme des activités sociales modelées sur la vie de tous les jours et la constituant. Il offre des concepts pour identifier la manière dont les structures sociales renforcent les activités routinières menant aux choix alimentaires. La structure sociale est examinée en utilisant les règles et les ressources de Giddens et est opérationnalisée de la façon suivante : systèmes de significations partagées, normes sociales, ressources matérielles et ressources d'autorité qui permettent ou empêchent les choix alimentaires désirés. Les résultats empiriques de deux études présentées dans cette thèse appuient la proposition que les choix alimentaires sont des pratiques sociales. La première étude examine les pratiques de choix alimentaires au sein des familles. Nous avons identifié les choix alimentaires comme cinq activités routinières distinctes intégrées dans la vie familiale de tous les jours à partir d’analyses réalisées sur les activités d’alimentation habituelles de 20 familles avec de jeunes enfants. Notre seconde étude a élaboré les règles et les ressources des pratiques alimentaires à partir des familles de l’étude. Ensuite, nous avons analysé la façon dont les règles et les ressources pouvaient expliquer les pratiques de choix alimentaires qui sont renforcées ou limitées au sein des familles lors de la routine spécifique à la préparation des repas et de la collation. Les ressources matérielles et d'autorité suffisantes ont permis d’expliquer les pratiques de choix alimentaires qui étaient facilitées, alors que les défis pouvaient être compris comme etant reliés à des ressources limitées. Les règles pouvaient empêcher ou faciliter les pratiques de choix alimentaires par l’entremise de normes ou de significations associées à la préparation de repas. Les données empiriques provenant de cette thèse appuient les choix alimentaires comme étant des activités routinières qui sont structurées socialement et qui caractérisent les familles. Selon la théorie de la structuration de Giddens, les pratiques routinières qui persistent dans le temps forment les institutions sociales. Ainsi, les pratiques routinières de choix alimentaires façonnent les styles d’habitudes alimentaires familiales et contribuent par ailleurs à la constitution des familles elles-mêmes. Cette compréhension identifie de nouvelles directions concernant la façon dont les choix alimentaires sont conceptualisés en santé publique. Les programmes de promotion de la santé destinés à améliorer la nutrition sont des stratégies clés pour prévenir les maladies chroniques et pour améliorer la santé populationnelle. Les choix alimentaires peuvent être abordés comme des activités partagées qui décrivent des groupes sociaux et qui sont socialement structurés par des règles et des ressources présentes dans les contextes de pratiques de choix alimentaires.

Some bivariate life time models in discrete time"

The term reliability of an equipment or device is often meant to indicate the probability that it carries out the functions expected of it adequately or without failure and within specified performance limits at a given age for a desired mission time when put to use under the designated application and operating environmental stress. A broad classification of the approaches employed in relation to reliability studies can be made as probabilistic and deterministic, where the main interest in the former is to device tools and methods to identify the random mechanism governing the failure process through a proper statistical frame work, while the latter addresses the question of finding the causes of failure and steps to reduce individual failures thereby enhancing reliability. In the probabilistic attitude to which the present study subscribes to, the concept of life distribution, a mathematical idealisation that describes the failure times, is fundamental and a basic question a reliability analyst has to settle is the form of the life distribution. It is for no other reason that a major share of the literature on the mathematical theory of reliability is focussed on methods of arriving at reasonable models of failure times and in showing the failure patterns that induce such models. The application of the methodology of life time distributions is not confined to the assesment of endurance of equipments and systems only, but ranges over a wide variety of scientific investigations where the word life time may not refer to the length of life in the literal sense, but can be concieved in its most general form as a non-negative random variable. Thus the tools developed in connection with modelling life time data have found applications in other areas of research such as actuarial science, engineering, biomedical sciences, economics, extreme value theory etc.

Face Recognition using Probabilistic Neural Networks

In this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results

Face Recognition using Probabilistic Neural Networks

n this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results.

Application of the european customer satisfaction index to postal services. Structural equation models versus partial least squars

Customer satisfaction and retention are key issues for organizations in today’s competitive market place. As such, much research and revenue has been invested in developing accurate ways of assessing consumer satisfaction at both the macro (national) and micro (organizational) level, facilitating comparisons in performance both within and between industries. Since the instigation of the national customer satisfaction indices (CSI), partial least squares (PLS) has been used to estimate the CSI models in preference to structural equation models (SEM) because they do not rely on strict assumptions about the data. However, this choice was based upon some misconceptions about the use of SEM’s and does not take into consideration more recent advances in SEM, including estimation methods that are robust to non-normality and missing data. In this paper, both SEM and PLS approaches were compared by evaluating perceptions of the Isle of Man Post Office Products and Customer service using a CSI format. The new robust SEM procedures were found to be advantageous over PLS. Product quality was found to be the only driver of customer satisfaction, while image and satisfaction were the only predictors of loyalty, thus arguing for the specificity of postal services

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression

Identification of plasmodium vivax proteins with potential role in invasion using sequence redundancy reduction and profile hidden markov models

Background: This study describes a bioinformatics approach designed to identify Plasmodium vivax proteins potentially involved in reticulocyte invasion. Specifically, different protein training sets were built and tuned based on different biological parameters, such as experimental evidence of secretion and/or involvement in invasion-related processes. A profile-based sequence method supported by hidden Markov models (HMMs) was then used to build classifiers to search for biologically-related proteins. The transcriptional profile of the P. vivax intra-erythrocyte developmental cycle was then screened using these classifiers. Results: A bioinformatics methodology for identifying potentially secreted P. vivax proteins was designed using sequence redundancy reduction and probabilistic profiles. This methodology led to identifying a set of 45 proteins that are potentially secreted during the P. vivax intra-erythrocyte development cycle and could be involved in cell invasion. Thirteen of the 45 proteins have already been described as vaccine candidates; there is experimental evidence of protein expression for 7 of the 32 remaining ones, while no previous studies of expression, function or immunology have been carried out for the additional 25. Conclusions: The results support the idea that probabilistic techniques like profile HMMs improve similarity searches. Also, different adjustments such as sequence redundancy reduction using Pisces or Cd-Hit allowed data clustering based on rational reproducible measurements. This kind of approach for selecting proteins with specific functions is highly important for supporting large-scale analyses that could aid in the identification of genes encoding potential new target antigens for vaccine development and drug design. The present study has led to targeting 32 proteins for further testing regarding their ability to induce protective immune responses against P. vivax malaria.

Option pricing in market models driven by telegraph processes with jumps

Esta tesis está dividida en dos partes: en la primera parte se presentan y estudian los procesos telegráficos, los procesos de Poisson con compensador telegráfico y los procesos telegráficos con saltos. El estudio presentado en esta primera parte incluye el cálculo de las distribuciones de cada proceso, las medias y varianzas, así como las funciones generadoras de momentos entre otras propiedades. Utilizando estas propiedades en la segunda parte se estudian los modelos de valoración de opciones basados en procesos telegráficos con saltos. En esta parte se da una descripción de cómo calcular las medidas neutrales al riesgo, se encuentra la condición de no arbitraje en este tipo de modelos y por último se calcula el precio de las opciones Europeas de compra y venta.

Small-area variation in health care affecting the choice of cesarean delivery: the case of a Colombian health insurer

In the midst of health care reform, Colombia has succeeded in increasing health insurance coverage and the quality of health care. In spite of this, efficiency continues to be a matter of concern, and small-area variations in health care are one of the plausible causes of such inefficiencies. In order to understand this issue, we use individual data of all births from a Contributory-Regimen insurer in Colombia. We perform two different specifications of a multilevel logistic regression model. Our results reveal that hospitals account for 20% of variation on the probability of performing cesarean sections. Geographic area only explains 1/3 of the variance attributable to the hospital. Furthermore, some variables from both demand and supply sides are found to be also relevant on the probability of undergoing cesarean sections. This paper contributes to previous research by using a hierarchical model and by defining hospitals as cluster. Moreover, we also include clinical and supply induced demand variables.

Factors influencing anthropogenic carbon dioxide uptake in the North Atlantic in models of the ocean carbon cycle

The uptake and storage of anthropogenic carbon in the North Atlantic is investigated using different configurations of ocean general circulation/carbon cycle models. We investigate how different representations of the ocean physics in the models, which represent the range of models currently in use, affect the evolution of CO2 uptake in the North Atlantic. The buffer effect of the ocean carbon system would be expected to reduce ocean CO2 uptake as the ocean absorbs increasing amounts of CO2. We find that the strength of the buffer effect is very dependent on the model ocean state, as it affects both the magnitude and timing of the changes in uptake. The timescale over which uptake of CO2 in the North Atlantic drops to below preindustrial levels is particularly sensitive to the ocean state which sets the degree of buffering; it is less sensitive to the choice of atmospheric CO2 forcing scenario. Neglecting physical climate change effects, North Atlantic CO2 uptake drops below preindustrial levels between 50 and 300 years after stabilisation of atmospheric CO2 in different model configurations. Storage of anthropogenic carbon in the North Atlantic varies much less among the different model configurations, as differences in ocean transport of dissolved inorganic carbon and uptake of CO2 compensate each other. This supports the idea that measured inventories of anthropogenic carbon in the real ocean cannot be used to constrain the surface uptake. Including physical climate change effects reduces anthropogenic CO2 uptake and storage in the North Atlantic further, due to the combined effects of surface warming, increased freshwater input, and a slowdown of the meridional overturning circulation. The timescale over which North Atlantic CO2 uptake drops to below preindustrial levels is reduced by about one-third, leading to an estimate of this timescale for the real world of about 50 years after the stabilisation of atmospheric CO2. In the climate change experiment, a shallowing of the mixed layer depths in the North Atlantic results in a significant reduction in primary production, reducing the potential role for biology in drawing down anthropogenic CO2.

Marine ecosystem models for earth systems applications: the MarQUEST experience

The MarQUEST (Marine Biogeochemistry and Ecosystem Modelling Initiative in QUEST) project was established to develop improved descriptions of marine biogeochemistry, suited for the next generation of Earth system models. We review progress in these areas providing insight on the advances that have been made as well as identifying remaining key outstanding gaps for the development of the marine component of next generation Earth system models. The following issues are discussed and where appropriate results are presented; the choice of model structure, scaling processes from physiology to functional types, the ecosystem model sensitivity to changes in the physical environment, the role of the coastal ocean and new methods for the evaluation and comparison of ecosystem and biogeochemistry models. We make recommendations as to where future investment in marine ecosystem modelling should be focused, highlighting a generic software framework for model development, improved hydrodynamic models, and better parameterisation of new and existing models, reanalysis tools and ensemble simulations. The final challenge is to ensure that experimental/observational scientists are stakeholders in the models and vice versa.

Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles

Process-based integrated modelling of weather and crop yield over large areas is becoming an important research topic. The production of the DEMETER ensemble hindcasts of weather allows this work to be carried out in a probabilistic framework. In this study, ensembles of crop yield (groundnut, Arachis hypogaea L.) were produced for 10 2.5 degrees x 2.5 degrees grid cells in western India using the DEMETER ensembles and the general large-area model (GLAM) for annual crops. Four key issues are addressed by this study. First, crop model calibration methods for use with weather ensemble data are assessed. Calibration using yield ensembles was more successful than calibration using reanalysis data (the European Centre for Medium-Range Weather Forecasts 40-yr reanalysis, ERA40). Secondly, the potential for probabilistic forecasting of crop failure is examined. The hindcasts show skill in the prediction of crop failure, with more severe failures being more predictable. Thirdly, the use of yield ensemble means to predict interannual variability in crop yield is examined and their skill assessed relative to baseline simulations using ERA40. The accuracy of multi-model yield ensemble means is equal to or greater than the accuracy using ERA40. Fourthly, the impact of two key uncertainties, sowing window and spatial scale, is briefly examined. The impact of uncertainty in the sowing window is greater with ERA40 than with the multi-model yield ensemble mean. Subgrid heterogeneity affects model accuracy: where correlations are low on the grid scale, they may be significantly positive on the subgrid scale. The implications of the results of this study for yield forecasting on seasonal time-scales are as follows. (i) There is the potential for probabilistic forecasting of crop failure (defined by a threshold yield value); forecasting of yield terciles shows less potential. (ii) Any improvement in the skill of climate models has the potential to translate into improved deterministic yield prediction. (iii) Whilst model input uncertainties are important, uncertainty in the sowing window may not require specific modelling. The implications of the results of this study for yield forecasting on multidecadal (climate change) time-scales are as follows. (i) The skill in the ensemble mean suggests that the perturbation, within uncertainty bounds, of crop and climate parameters, could potentially average out some of the errors associated with mean yield prediction. (ii) For a given technology trend, decadal fluctuations in the yield-gap parameter used by GLAM may be relatively small, implying some predictability on those time-scales.

Food choice in an interdisciplinary context

Reform of agricultural policies, notably the continuing elimination of production-enhancing subsidies, makes it possible for policies to respond to social issues such as the rural environment and health in future. In this paper, we draw on a Rural Economy and Land Use (RELU) research project which is examining the potential for the development of healthy food chains and the implications for human health and the environment. One of the key issues to be addressed is consumers' willingness to pay for the nutritionally enhanced food products from these new chains, but it is evident that only a partial understanding can be gained from a traditional economics approach. In the paper, we discuss how economists are beginning to incorporate views from other disciplines into their models of consumer choice.

Describing long-term trends in precipitation using generalized additive models

With the current concern over climate change, descriptions of how rainfall patterns are changing over time can be useful. Observations of daily rainfall data over the last few decades provide information on these trends. Generalized linear models are typically used to model patterns in the occurrence and intensity of rainfall. These models describe rainfall patterns for an average year but are more limited when describing long-term trends, particularly when these are potentially non-linear. Generalized additive models (GAMS) provide a framework for modelling non-linear relationships by fitting smooth functions to the data. This paper describes how GAMS can extend the flexibility of models to describe seasonal patterns and long-term trends in the occurrence and intensity of daily rainfall using data from Mauritius from 1962 to 2001. Smoothed estimates from the models provide useful graphical descriptions of changing rainfall patterns over the last 40 years at this location. GAMS are particularly helpful when exploring non-linear relationships in the data. Care is needed to ensure the choice of smooth functions is appropriate for the data and modelling objectives. (c) 2008 Elsevier B.V. All rights reserved.

Different splitting techniques with application to air pollution models

Splitting techniques are commonly used when large-scale models, which appear in different fields of science and engineering, are treated numerically. Four types of splitting procedures are defined and discussed. The problem of the choice of a splitting procedure is investigated. Several numerical tests, by which the influence of the splitting errors on the accuracy of the results is studied, are given. It is shown that the splitting errors decrease linearly when (1) the splitting procedure is of first order and (2) the splitting errors are dominant. Three examples for splitting procedures used in all large-scale air pollution models are presented. Numerical results obtained by a particular air pollution model, Unified Danish Eulerian Model (UNI-DEM), are given and analysed.