Derivation of empirical equations for neutronic performance in a thorium fusion breeder with various coolants using regression analysis

In this paper, regression analyses (RA) are presented for the neutronic calculation of ThO₂ mixed ²⁴⁴CmO₂ fuel with different neutronic parameters for various coolants, natural lithium, Li₂₀Sn₈₀ and Flinabe, respectively. The tritium breeding ratio (TBR), energy multiplication factor (M), total fission rate (Σ_f) and ²³²Th(n, γ) reaction is computed by XSDRNPM. In addition, this numerical results are estimated by RA depends on neutronic parameters and the empirical equations for neutronic performance are acquired. The results obtained by using XSDRNPM and the results of the RA, obtained empirical equations, are compared. The empirical equations indicate that RA can successfully be used for the prediction of the neutronic performance parameters in the hybrid reactor with a high degree of accuracy. In addition, correlation matrix is calculated to determined statistical relationships between variables TBR, M, Σ_f, and ²³²Th(n, γ).     

Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm

In this paper, we present a new system for the classification of electrocardiogram (ECG) beats by using a fast least square support vector machine (LSSVM). Five feature extraction methods are comparatively examined in the 15-dimensional feature space. The dimension of the each feature set is reduced by using dynamic programming based on divergence analysis. After the preprocessing of ECG data, six types of ECG beats obtained from the MIT-BIH database are classified with an accuracy of 95.2% by the proposed fast LSSVM algorithm together with discrete cosine transform. Experimental results show that not only the fast LSSVM is faster than the standard LSSVM algorithm, but also it gives better classification performance than the standard backpropagation multilayer perceptron network.     

Initial microstructure and retained austenite in 8 Mn steel controlled by cooling rate

The present work investigates the effect of the initial microstructure on phase transformation after intercritical annealing by measuring the amount of austenite, which was obtained by X-ray diffraction and saturation magnetisation. Pieces of 8?Mn steel were austenitised at 1100°C for 1?h followed by different cooling rates: water, air, and furnace. Samples of each piece were subsequently intercritically annealed from 600 to 800°C followed by air cooling. The microstructure was characterised using scanning electron microscopy and electron backscatter diffraction. Results show how changing the cooling rate affects the temperature of intercritical annealing at which the highest content of retained austenite was obtained.     

Supplementation of amino acids in apple must for the standardization of volatile compounds in ciders

The aim of this study was to identify the main amino acids responsible for the production of esters in ciders. The experiments were carried out in apple must and synthetic must. The amino acids were analysed by high‐performance liquid chromatography and the volatile compounds by gas chromatography. Aspartate, asparagine and glutamate positively influenced the production of esters in the cider models and were used in the experiments with mixtures. The response surface generated by the proposed model yielded two regions that maximized the production of esters. In addition, the combination of aspartate and glutamate predicted a higher production. The optimal suggested concentrations were 43.4% of aspartate and 56.6% of glutamate for 120 mg/L of total nitrogen. The apple must supplemented with these two amino acids resulted in four times more esters than in the same cider without supplementation, demonstrating the effectiveness of the proposed model.     

The challenge of evaluating health effects of organic food; operationalisation of a dynamic concept of health

The health benefits of consuming organically produced foods compared with conventional foods are unclear. Important obstacles to drawing clear conclusions in this field of research are (1) the lack of a clear operational definition of health and (2) the inability to distinguish between different levels of health using valid biomarkers. In this paper, some shortcomings of the current definition of health are outlined and the relevance of integrating a more dynamic and functional component is emphasised, which is reflected by the ability to adapt. The state of health could then be determined by challenging an individual with some form of stressor and by subsequent quantification and evaluation of the coherence in recovery of various physiological processes and parameters. A set of relevant parameters includes the activity of the immune system and the activity of the autonomous nervous system. A good recovery towards homeostasis is suggested to reflect a qualitatively good state of health. Furthermore, it would enable objective evaluation of health-optimising strategies, including the consumption of organically produced foods that aim to strengthen health. Copyright ? 2012 Society of Chemical Industry.     

Work satisfaction of health personnel in primary care

OBJECTIVE: To determine the level of work satisfaction of health professionals working in Primary Care and to establish the social, demographic and professional factors which determine it. DESIGN: An observational, crossover study using a self-administered questionnaire. SETTING: Primary Care. PARTICIPANTS: All the doctors and nurses working in the Primary Care teams in the Albacete Health Area (468 in all). MEASUREMENTS AND MAIN RESULTS: The scale of work satisfaction of health professionals in Primary Care teams was used. Social, demographic and professional data were collected. 9 dimensions or components of work satisfaction were identified through a factorial analysis. The lowest scores were for motivation, opportunities for professional development and coordination with specialists. CONCLUSIONS: The findings show, in general, indifference as to work satisfaction or lack of it in areas such as motivation and opportunities for professional development. The differences observed in those polled relate to their job and work-place, and also, in the case of doctors, to specialist training.     

Modeling of Closed-Vessel Experiments and Ballistic Parameter Estimation

ABSTRACT

Closed vessels have been used for the regression of lumped ballistic parameters for decades. If material and energy balances are coupled with burning rate empirical correlations, uncorrelated parameters can be adjusted, describing accurately the thermochemical behavior of the generated gases, even if the chemical composition of the propellant is unknown (aging of the propellant). This work presents a system of differential Equations integrated to produce pressure profiles within the vessel that are highly dependent on the choice of empirical parameters. Such parameters are manipulated via maximum likelihood statistical procedures, leading to the best set of parameters to describe the propellant.     

Automatic recognition of sleep spindles in EEG via radial basis support vector machine based on a modified feature selection algorithm

This paper presents an application of a radial basis support vector machine (RB-SVM) to the recognition of the sleep spindles (SSs) in electroencephalographic (EEG) signal. The proposed system comprises of two stages. In the first stage, for feature extraction, a set of raw amplitude values, a set of discrete cosine transform (DCT) coefficients, a set of discrete wavelet transform (DWT) approximation coefficients and a set of adaptive autoregressive (AAR) parameters are calculated and extracted from signals separately as four different sets of feature vectors. Thus, four different feature vectors for the same data are comparatively examined. In the second stage, these features are then selected by a modified adaptive feature selection method based on sensitivity analysis, which mainly supports input dimension reduction via selecting the most significant feature elements. Then, the feature vectors are classified by a support vector machine (SVM) classifier, which is relatively new and powerful technique for solving supervised binary classification problems due to its generalization ability. Visual evaluation, by two electroencephalographers (EEGers), of 19 channel EEG records of six subjects showed that the best performance is obtained with an RB-SVM providing an average sensitivity of 97.7%, an average specificity of 97.4% and an average accuracy of 97.5%.