305 research outputs found

    E-Learning for Teachers and Trainers : Innovative Practices, Skills and Competences

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    Reproduction is authorised provided the source is acknowledged.Final Published versio

    Effect of Modifiers on the Microstructure of Rapidly Solidified AlSi10Mg Alloy

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    In this work, melt-spun ribbons of AlSi10Mg added with modifiers (Er, Sr, or nano-TiB2) were produced to investigate the combined effect of modification and rapid solidification on eutectic Si. The resulting eutectic microstructures are more isotropic in comparison to that of the base alloy affecting the mechanical properties of the alloys. The modification of Si morphology and supersaturation caused by the modifiers were investigated by microscopy, X-ray diffraction, and differential scanning calorimetry. Compared to melt-spun AlSi10Mg, the eutectic Si network is finer and less continuous when Er or Sr is added, and disrupted with rounded crystals dispersed in the matrix when adding nano-TiB2. The level of supersaturation decreases in the order Er–nano-TiB2–Sr. A transition from columnar Al grains at the wheel side to finer equiaxed grains at the air side was found in the unmodified ribbon and in the one containing nano-TiB2 by means of electron backscattered diffraction. The Er- and Sr-modified ribbons display equiaxed Al grains of constant size throughout their thickness. The average hardness obtained by nano-indentation tests was lower than that of AlSi10Mg. The less continuous Si network causes the hardness drop but provides more isotropic mechanical properties

    Prediction of Resting Energy Expenditure in Children: May Artificial Neural Networks Improve Our Accuracy?

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    The inaccuracy of resting energy expenditure (REE) prediction formulae to calculate energy metabolism in children may lead to either under- or overestimated real caloric needs with clinical consequences. The aim of this paper was to apply artificial neural networks algorithms (ANNs) to REE prediction. We enrolled 561 healthy children (2-17 years). Nutritional status was classified according to World Health Organization (WHO) criteria, and 113 were obese. REE was measured using indirect calorimetry and estimated with WHO, Harris-Benedict, Schofield, and Oxford formulae. The ANNs considered specific anthropometric data to model REE. The mean absolute error (mean \ub1 SD) of the prediction was 95.8 \ub1 80.8 and was strongly correlated with REE values (R2 = 0.88). The performance of ANNs was higher in the subgroup of obese children (101 \ub1 91.8) with a lower grade of imprecision (5.4%). ANNs as a novel approach may give valuable information regarding energy requirements and weight management in children
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