Abstract
This article describes a Kernel Principal Component Regressor (KPCR) to identify Auto Regressive eXogenous (ARX) Linear Parmeter Varying (LPV) models. The new method differs from the Least Squares Support Vector Machines (LS-SVM) algorithm in the regularisation of the Least Squares (LS) problem, since the KPCR only keeps the principal components of the Gram matrix while LS-SVM performs the inversion of the same matrix after adding a regularisation factor. Also, in this new approach, the LS problem is formulated in the primal space but it ends up being solved in the dual space overcoming the fact that the regressors are unknown. The method is assessed and compared to the LS-SVM approach through 2 Monte Carlo (MC) experiments. Every experiment consists of 100 runs of a simulated example, and a different noise level is used in each experiment,with Signal to Noise Ratios of 20db and 10db, respectively. The obtained results are twofold, first the performance of the new method is comparable to the LS-SVM, for both noise levels, although the required calculations are much faster for the KPCR. Second, this new method reduces the dimension of the primal space and may convey a way of knowing the number of basis functions required in the Kernel. Furthermore, having a structure very similar to LS-SVM makes it possible to use this method in other types of models, e.g. the LPV state-space model identification.

Abstract

Abstract
WiFi (IEEE 802.11 standard) networks are widely used to support real-time (RT) applications, from home environment systems to complex networked control systems (NCS). Nevertheless, the Quality of Service (QoS) extensions incorporated into the standard are still unable to guarantee some relevant RT communications requirements. This paper presents an experimental validation of the RT-WiFi architecture that was recently proposed to deal with RT communication requirements and analysed through simulation. The experimental results demonstrate the feasibility of implementing the RT-WiFi architecture and improving the QoS level of communications through a comparative analysis with the EDCA (Enhanced Distributed Channel Access) mechanism, which is a mechanism incorporated in the IEEE 802.11 standard to provide different levels of transmission priority of different types of traffic.

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Abstract
This study assesses in vivo and noninvasive human lower limb dynamic stiffness at different muscle stretch-shortening cycle (SSC) on maximum vertical jump (MVJ) with long, short and without countermovement. A small sample of n=6 healthy young adult male students of sports and physical education degree without past injuries, specific training or sports record were assessed after informed consent according to the world medical association declaration of Helsinki. Subjects were weighted (76.7 +/- 9.3) kg and their height measured (178.9 +/- 6.3) cm performing each three trials of MVJ namely squat jump (SJ), countermovement jump (CMJ) and drop jump (DJ) from a step with 40 cm height. Ground reaction forces (GRF) were acquired during impulse phase with AMTI force plate BP2416-4000 CE and Mini Amp MAS-6 amplifier at 1000 Hz. Vertical acceleration (a(z)) of whole body center of gravity (BCG) obtained from resultant vertical ground reaction force (RFz) was time integrated twice for calculation of vertical displacement (Delta z) of BCG and dynamic stiffness obtained from slopes at different segmented subphases of RFz mass normalized RFz/m vs Delta z plot during impulse phase. Results of vertical dynamic stiffness during impulse phase point for inter-subject similarities at each type of MVJ and statistical significative differences (p<0.05) between SJ, CMJ and DJ with impact on MVJ optimization.

Abstract
The National School Feeding Program - NSFP is the public health policy that guarantees the minimum supply of healthy food and offers space for the construction of healthy eating habits in Brazilian schools. The objective of the present study was to evaluate the presence of Food and Nutrition Education (FNE) actions within the framework of the NSFP to verify its adequacy toward the program's legislation and to investigate which parameters are predictors of running school gardens. This was a cross-sectional observational study using secondary data obtained from the database of the Efficient School Lunch Management Award for the year 2010, with a sample of 749 municipalities in Brazil. A binary logistic regression was performed, considering the running of school gardens as a dependent variable, and the FNE indicators and the demographic variables as independent. The model containing the presence of FNE in the school curriculum was significant (p<0.001). The presence of FNE in school curricula OR=2.406;95%CI=[1.725, 3.357] was the most significant predictor of running school gardens, followed by the use of food from family farms OR=2.049;95%CI=[1.477, 2.842] and the carrying out of culinary workshops OR=2.032;95%CI=[1.442; 2.863], considering p<0.05. The presence of FNE in the school curriculum was positively associated with growing vegetable gardens, showing that complex actions that require more resources can be stimulated from simpler measures, and the NSFP as a public health policy is an important tool to promote actions throughout the national territory.