De nombreux constats contemporains s’alarment du malaise récurrent des ressources humaines hospitalières, particulièrement à l’endroit des médecins et des soignants, et par conséquent du risque de mauvaise qualité de prise en charge des patients. Adoptant une approche plus optimiste, des chercheurs américains en soins infirmiers ont mis en évidence depuis le début des années 1980 l’existence d’hôpitaux dits magnétiques, parce qu’attractifs et fidélisateurs, et où il ferait bon travailler et se faire soigner. Cette thèse vise à approfondir le concept de Magnet Hospital, à éclairer sa définition et sa portée pour la gestion des ressources humaines hospitalières en France. Suivant une démarche hypothético-déductive, la conceptualisation, fondée sur un état de l’art, débute par une appropriation du modèle synthétique du Magnet Hospital. Empruntant une perspective psychosociale, notre modèle original de recherche se focalise sur la perception, à l’échelle des unités de soins, des attributs managériaux du magnétisme hospitalier (leadership transformationnel, empowerment perçu de la participation et climat relationnel collégial entre médecins et soignants) et ses conséquences attitudinales positives (satisfaction, implication, intention de rester, équilibre émotionnel travail/hors travail et efficacité collective perçue). Une méthodologie quantitative interroge au moyen de 8 échelles ad hoc un échantillon représentatif de 133 médecins, 361 infirmières et 362 aides-soignantes de 36 services de médecine polyvalente français. Une série de modélisations par équations structurelles, selon l’algorithme Partial Least Squares, teste la nature et l’intensité des relations directes et indirectes du magnétisme managérial perçu. Les résultats statistiques indiquent une bonne qualité des construits et d’ajustement des modèles. Un contexte managérial magnétique produit son principal effet positif sur l’efficacité collective perçue. Des différences catégorielles existent quant à la perception de sa composition et à la transmission de ses effets par la médiation de l’efficacité collective perçue, signalant le caractère contingent du magnétisme. Ces résultats ouvrent des perspectives managériales et scientifiques, en soulignant l’intérêt des approches positives de l’organisation hospitalière. / Many contemporary findings are alarmed of the recurring discomfort of hospital human resources, especially against doctors and nurses, and consequently against risk of poor quality of care for patients. Adopting a more optimistic approach, American nursing scholars have highlighted since the 1980s, some magnet hospitals, able to attract and retain, and with good working and care conditions. This thesis aims to explore Magnet Hospital concept, to inform its definition and scope for hospital human resource management in France. According to a hypothetico-deductive approach, based on a review of the literature, the conceptualization begins with appropriation of synthetic Magnet Hospital model. Under a psychosocial perspective, our original research model focuses on perception of managerial magnetic attributes (transformational leadership, perceived empowerment of participation, collegial climate between doctors and nurses) and their consequences on positive job attitudes (satisfaction, commitment, intent to rest, emotional equilibrium work/family, perceived collective efficacy), at wards level. A quantitative methodology proceeds by a questionnaire of 8 ad hoc scales and interviews 133 doctors, 361 nurses, 362, auxiliary nurses, in 36 French medicine units. A set of structural equations modeling, according to Partial Least Squares, tests nature and intensity of direct and indirect relationships of perceived managerial magnetism. The statistical results show a good validity of constructs and a good fit of models. The major positive effect of magnetic managerial context is on perceived collective efficacy. Some professional differences exist about perceptions of composition and transmission of magnetic effects (via mediation of perceived collective efficacy), indicating the contingency of magnetism. These findings open managerial and scientific opportunities, emphasizing the interest for positive organizational approach of hospital.

The concept of customer perceived value occupies a prominent position within the strategic agenda of organisations, as firms seek to maximise the value perceived by their customers as arising from their consumption, and to equal or exceed that perceived in relation to competitor propositions. Customer value management is similarly central to the marketing discipline. However, the nature of customer value remains ambiguous and its measurement is typically flawed, due to the poor conceptual foundation upon which previous research endeavours are built. This investigation seeks to address the current poverty of insight regarding the nature and measurement of customer value. The development of a revised conceptual framework synthesises the strengths of previous value conceptualisations while addressing many of their limitations. A multi-dimensional depiction of value arising from customer experience is presented, in which value is conceptualised as arising at both first-order dimension and overall, second-order levels of abstraction. The subsequent operationalisation of this conceptual framework within a two-phase investigation combines qualitative and quantitative methodologies in a study of customer value arising from subscription TV (STV) consumption. Sixty semi-structured interviews with 103 existing STV customers give rise to a multi-dimensional model of value, in which dimensions are categorised as restorative, actualising and hedonic in type, and as arising via individual, reflected or shared modes of perception. The quantitative investigation entails two periods of data collection via questionnaires developed from the qualitative findings, and the gathering of 861 responses, also from existing STV customers. A series of scales with which to measure value dimensions is developed and an index enabling overall perceived value measurement is produced. Contributions to theory of customer value arise in the form of enhanced insights regarding its nature. At the first-order dimension level, the derived dimensions are of specific relevance to the STV industry. However, the empirically derived framework of dimension types and modes of perception has potential applicability in multiple contexts. At the more abstract, second-order level, the findings highlight that value perceptions comprise only a subset of potential dimensions. Evidence is thus presented of the need to consider value at both dimension and overall levels of perception. Contributions to knowledge regarding customer value measurement also arise, as the study produces reliable and valid scales and an index. This latter tool is novel in its formative measurement of value as a second order construct, comprising numerous first-order dimensions of value, rather than quality as incorporated in previously derived measures. This investigation also results in a contribution to theory regarding customer experience through the identification of a series of holistic, discrete, direct and indirect value-generating interactions. Contributions to practice within the STV industry arise as the findings present a solution to the immediate need for enhanced value insight. Contributions to alternative industries are methodological, as this study presents a detailed process through which robust value insight can be derived. Specific methodological recommendations arise in respect of the need for empirically grounded research, an experiential focus and a twostage quantitative methodology.

[pt] Visando determinar os teores dos componentes relevantes e as propriedades físicas de gasolinas comerciais e sintéticas foram tomados espectros Raman de 60 gasolinas comerciais e 52 misturas sintéticas simulando gasolinas. Os espectros foram tomados em um espectrômetro Nicolet FT Raman 950. Os espectros brutos obtidos foram tratados para evitar a influência da variabilidade de potência do laser excitante sobre as intensidades das linhas Raman. As variáveis independentes (intensidades Raman ) e as variáveis dependentes (propriedades das gasolinas comerciais e misturas sintéticas ) foram centradas em torno da média e submetidas à regressão por mínimos quadrados parciais, visando ajustar modelos que permitissem predizer quantitativamente os teores de etanol, hidrocarbonetos saturados, insaturados e aromáticos além dos valores das propriedades MON, RON, densidade e pontos de ebulição inicial, final, a 10%, 50% e 90% das amostras em estudo. Os resultados obtidos mostraram a potencialidade da espectroscopia Raman, para o desenvolvimento de métodos confiáveis para a análise de diversas características das gasolinas estudadas. / [en] The aim of this work was to determine the contents of the more important components and physical properties of commercial gasolines and synthetic mixtures with known composition, prepared in the laboratory. The Raman spectra of 60 gasolines and 52 mixtures were acquired with a Nicolet 950 Fourier Transform Raman (FT-Raman) spectrometer. The raw spectra were treated to avoid the laser potency variability on Raman lines intensities. The independent variables (Raman intensities) and the dependent variables (gasolines and mixtures properties) were mean centered and models were fit by partial least square regression seeking to predict the contents of ethanol, saturated, unsaturated and aromatic hydrocarbons. Also properties as MON, RON, density and boiling point values were determined by this procedure. The final results showed the potential of Raman spectroscopy for analysis of several properties of gasolines.

This thesis describes the research undertaken for the degree of Doctor of Philosophy, testing the hypothesis that spectrometer data can be used to establish usable relationships for prediction of pasture quality attributes. The research data consisted of reflectance measurements of various temperate pasture types recorded at four different times (years 2000 to 2002), recorded by three hyperspectral sensors, the in situ ASD, the airborne HyMap and the satellite-borne Hyperion. Corresponding ground-based pasture samples were analysed for content of chlorophyll, water, crude protein, digestibility, lignin and cellulose at three study sites in rural Victoria, Australia. This context was used to evaluate effects of sensor differences, data processing and enhancement, analytical methods and sample variability on the predictive capacity of derived prediction models. Although hyperspectral data analysis is being applied in many areas very few studies on temperate pastures have been conducted and hardly any encompass the variability and heterogeneity of these southern Australian examples. The research into the relationship between the spectrometer data and pasture quality attribute assays was designed using knowledge gained from assessment of other hyperspectral remote sensing and near-infrared spectroscopy research, including bio-chemical and physical properties of pastures, as well as practical issues of the grazing industries and carbon cycling/modelling. Processing and enhancement of the spectral data followed methods used by other hyperspectral researchers with modifications deemed essential to produce better relationships with pasture assay data. As many different methods are in use for the analysis of hyperspectral data several alternative approaches were investigated and evaluated to determine reliability, robustness and suitability for retrieval of temperate pasture quality attributes. The analyses employed included stepwise multiple linear regression (SMLR) and partial least squares regression (PLSR). The research showed that the spectral research data had a higher potential to be used for prediction of crude protein and digestibility than for the plant fibres lignin and cellulose. Spectral transformation such as continuum removal and derivatives enhanced the results. By using a modified approach based on sample subsets identified by a matrix of subjective bio-physical and ancillary data parameters, the performance of the models were enhanced. Prediction models from PLSR developed on ASD in situ spectral data, HyMap airborne imagery and Hyperion and corresponding pasture assays showed potential for predicting the two important pasture quality attributes crude protein and digestibility in hyperspectral imagery at a few quantised levels corresponding to levels currently used in commercial feed testing. It was concluded that imaging spectrometry has potential to offer synoptic, simultaneous and spatially continuous information valuable to feed based enterprises in temperate Victoria. The thesis provide a significant contribution to the field of hyperspectral remote sensing and good guidance for future hyperspectral researchers embarking on similar tasks. As the research is based on temperate pastures in Victoria, Australia, which are dominated by northern hemisphere species, the findings should be applicable to analysis of temperate pastures elsewhere, for example in Western Australia, New Zealand, South Africa, North America, Europe and northern Asia (China).

<p>The paper describes predictive modelling of biomarker data stemming from patients suffering from multiple sclerosis. Improvements of multivariate analyses of the data are investigated with the goal of increasing the capability to assign samples to correct subgroups from the data alone.</p><p>The effects of different preceding scalings of the data are investigated and combinations of multivariate modelling methods and variable selection methods are evaluated. Attempts at merging the predictive capabilities of the method combinations through voting-procedures are made. A technique for improving the result of PLS-modelling, called bagging, is evaluated.</p><p>The best methods of multivariate analysis of the ones tried are found to be Partial least squares (PLS) and Support vector machines (SVM). It is concluded that the scaling have little effect on the prediction performance for most methods. The method combinations have interesting properties – the default variable selections of the multivariate methods are not always the best. Bagging improves performance, but at a high cost. No reasons for drastically changing the work flows of the biomarker data analysis are found, but slight improvements are possible. Further research is needed.</p>

Problem: Real-time process and destructive test data were collected from a wood composite manufacturer in the U.S. to develop real-time predictive models of two key strength properties (Modulus of Rupture (MOR) and Internal Bound (IB)) of a wood composite manufacturing process. Sensor malfunction and data “send/retrieval” problems lead to null fields in the company’s data warehouse which resulted in information loss. Many manufacturers attempt to build accurate predictive models excluding entire records with null fields or using summary statistics such as mean or median in place of the null field. However, predictive model errors in validation may be higher in the presence of information loss. In addition, the selection of predictive modeling methods poses another challenge to many wood composite manufacturers. Approach: This thesis consists of two parts addressing above issues: 1) how to improve data quality using missing data imputation; 2) what predictive modeling method is better in terms of prediction precision (measured by root mean square error or RMSE). The first part summarizes an application of missing data imputation methods in predictive modeling. After variable selection, two missing data imputation methods were selected after comparing six possible methods. Predictive models of imputed data were developed using partial least squares regression (PLSR) and compared with models of non-imputed data using ten-fold cross-validation. Root mean square error of prediction (RMSEP) and normalized RMSEP (NRMSEP) were calculated. The second presents a series of comparisons among four predictive modeling methods using imputed data without variable selection. Results: The first part concludes that expectation-maximization (EM) algorithm and multiple imputation (MI) using Markov Chain Monte Carlo (MCMC) simulation achieved more precise results. Predictive models based on imputed datasets generated more precise prediction results (average NRMSEP of 5.8% for model of MOR model and 7.2% for model of IB) than models of non-imputed datasets (average NRMSEP of 6.3% for model of MOR and 8.1% for model of IB). The second part finds that Bayesian Additive Regression Tree (BART) produced most precise prediction results (average NRMSEP of 7.7% for MOR model and 8.6% for IB model) than other three models: PLSR, LASSO, and Adaptive LASSO.

Stimulus-driven changes in the gain of sensory neurons are well-documented, but relatively little is known about whether analogous gain-control can also be effected in a top-down manner. A recent psychophysical study demonstrated that sensitivity to luminance contrast can be modulated by a priori knowledge (de la Rosa et al., in press). In the present study, event-related potentials were used to resolve the stages of information processing that facilitate such knowledge-driven adjustments. Groupwise independent component analysis identified two robust spatiotemporal patterns of endogenous brain activity that captured experimental effects. The first pattern was associated with obligatory processing of contextual information, while the second pattern was associated with selective initiation of contrast gain adjustment. These data suggest that knowledge-driven contrast gain control is mediated by multiple independent electrogenic sources.

Stimulus-driven changes in the gain of sensory neurons are well-documented, but relatively little is known about whether analogous gain-control can also be effected in a top-down manner. A recent psychophysical study demonstrated that sensitivity to luminance contrast can be modulated by a priori knowledge (de la Rosa et al., in press). In the present study, event-related potentials were used to resolve the stages of information processing that facilitate such knowledge-driven adjustments. Groupwise independent component analysis identified two robust spatiotemporal patterns of endogenous brain activity that captured experimental effects. The first pattern was associated with obligatory processing of contextual information, while the second pattern was associated with selective initiation of contrast gain adjustment. These data suggest that knowledge-driven contrast gain control is mediated by multiple independent electrogenic sources.

Machine learning methods have been promising tools in science and engineering fields. The use of these methods in chemistry and drug design has advanced after 1990s. In this study, molecular electrostatic potential (MEP) surfaces of PCP-like compounds are modelled and visualized in order to extract features which will be used in predicting binding affinity. In modelling, Cartesian coordinates of MEP surface points are mapped onto a spherical self-organizing map. Resulting maps are visualized by using values of electrostatic potential. These values also provide features for prediction system. Support vector machines and partial least squares method are used for predicting binding affinity of compounds, and results are compared.

The e-government phenomena has become more important with the ever increasing number of implementations world wide. A model explaining the e-government adoption and the related measurement instrument a survey had been developed and validated in this study. In a post technology acceptance model (TAM) approach, theory of planned behavior (TPB) was extended to t the requirements of e-government context. The adoption of student loans service of the higher education student loans and accommodation association (KYK) was investigated to obtain data for empirical validation. The instrument was administered to over four-hundred students and partial least squares path modeling was employed to analyze the data. The results indicate that the model was an improvement over TAM in terms of predictive power. The constructs investigated in the study successfully explained the intention to use an e-government service.