Prevalence and magnitude of body weight and image dissatisfaction among women in dietetics majors

[EN] Some authors have suggested that body weight dissatisfaction may be high in students majoring in dietetics. Therefore, this study was conducted to examine the extent of body weight and image dissatisfaction in a sample of women in dietetics major. Additionally, predictors of magnitude of body weight dissatisfaction were analyzed. Participants were 62 volunteers with normalweight whose mean age was 21.87±1.89 years old (nonrandom sample). The assessment instruments included anthropometric measurements, a somatomorphic matrix test and an eating disorders inventory (EDI-2). Data were analyzed using SPSS vs. 15.0. A larger proportion of students chose an ideal body weight lower than actual weight (67.7%) and body image with less body fat and more muscle mass than actual values (56.4%). The magnitude of body weight dissatisfaction was associated with muscle mass and body fat dissatisfaction, and with the subscale of EDI-2 “body dissatisfaction”. So, from a public health standpoint, we consider important to continue working in this line of research with the aim of better understanding the extent of body weight dissatisfaction in women dietitians, and how this dissatisfaction could interfere with their professional practice.

Measurement of polycyclic aromatic hydrocarbons in ambient air by fluorescence spectroscopic techniques

250 p. + anexos

Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.