Abstract
Waist circumference (WC) has been associated with cardiometabolic diseases. We aim to develop WC references for age and gender in youngsters in Autonomous Region of Madeira, Portugal (RAM). A cross-sectional study was developed in two phases. Between May 2004 and May 2005 (in 1st cycle schools) and between October 2007 and June 2009 (in 2nd and 3rd cycle, and secondary and professional schools). A representative sample of 6987 individuals was gathered, with 3532 girls and 3455 boys aged from 6 to 18 years old. WC was measured with a non-elastic tape at the midpoint between the iliac crest and the last rib. To elaborate smoothed percentiles and z-scores curves for age and gender, values were estimated between 72 and 225 months (every three months) using the LMS statistical procedure, with the exponent of the Box-Cox (L), the median (M), and coefficient of variation generalized (S) through LMSChartMaker software, version 2.76. Mean and standard deviation and charts of smoothed percentiles curves (P3, P5, P10, P25, P50, P75, P85, P90, P95 and P97) and z-scores (-2, -1, 0, 1 and 2) are presented by age and gender. These references constitute a new tool to assess cardiometabolic risk of RAM's children and adolescents.

Abstract
The coordination problem in multi-AGV systems can be approached as an optimization problem and aims to make possible the execution of several tasks simultaneously, avoiding collision and deadlock situations and reducing the average execution time. Time Enhanced A* (TEA*) is one of the path planning algorithms developed for this purpose. This paper focus on the implementation of the TEA* for real industrial applications. In that context, a new approach was developed to complement the TEA* with the capacity to approximate the planning of the future positions for differential robots with its real behaviour.

Abstract
Diagnosis of Fabry disease (FD) remains a challenge mostly due to its rare occurrence and phenotipical variability, with considerable delay between onset and clinical diagnosis. It is then of extreme importance to explore biomarkers capable of assisting the earlier diagnosis of FD. There is growing evidence supporting the use of gait assessment in the diagnosis and management of several neurological diseases. In fact, gait abnormalities have previously been observed in FD, justifying further investigation. The aim of this study is to evaluate the effectiveness of different machine learning strategies when distinguishing patients with FD from healthy controls based on normalized gait features. Gait features of an individual are affected by physical characteristics including age, height, weight, and gender, as well as walking speed or stride length. Therefore, in order to reduce bias due to inter-subject variations a multiple regression (MR) normalization approach for gait data was performed. Four different machine learning strategies - Support Vector Machines (SVM), Random Forest (RF), Multiple Layer Perceptrons (MLPs), and Deep Belief Networks (DBNs) - were employed on raw and normalized gait data. Wearable sensors positioned on both feet were used to acquire the gait data from 36 patients with FD and 34 healthy subjects. Gait normalization using MR revealed significant differences in percentage of stance phase spent in foot flat and pushing (p < 0.05), with FD presenting lower percentages in foot flat and higher in pushing. No significant differences were observed before gait normalization. Support Vector Machine was the superior classifier achieving an FD classification accuracy of 78.21% after gait normalization, compared to 71.96% using raw gait data. Gait normalization improved the performance of all classifiers. To the best of our knowledge, this is the first study on gait classification that includes patients with FD, and our results support the use of gait assessment on the clinical assessment of FD. © 2019 IEEE.

Abstract
Smart home devices currently available on the market can be used for remote monitoring and control. Energy management systems can take advantage of this and deploy solutions that can be implemented in our homes. One of the big enablers is smart plugs that allow the control of electrical resources while providing a retrofitting solution, hence avoiding the need for replacing the electrical devices. However, current so-called smart plugs lack the ability to understand the environment they are in, or the electrical appliance/resource they are controlling. This paper applies environment awareness smart plugs (EnAPlugs) able to provide enough data for energy management systems or act on its own, via a multi-agent approach. A case study is presented, which shows the application of the proposed approach in a house where 17 EnAPlugs are deployed. Results show the ability to shared knowledge and perform individual resource optimizations. This paper evidences that by integrating artificial intelligence on devices, energy advantages can be observed and used in favor of users, providing comfort and savings.

Abstract
Cooperative vehicle platooning applications increasingly demand realistic simulation tools to ease their validation, and to bridge the gap between development and real-word deployment. However, their complexity and cost, often hinders its validation in the real-world. In this paper we propose a realistic simulation framework for vehicular platoons that integrates Gazebo with OMNeT++ over Robot Operating System (ROS) to support the simulation of realistic scenarios of autonomous vehicular platoons and their cooperative control.

Abstract
Wi-Fi networks are becoming more and more ubiquitous and represent a substantial source of energy consumption around the globe, mainly when it comes to Access Points (APs). There has been some work done on the characterization of the power consumption of Wi-Fi APs and network interface cards (NICs), and the power usage of these devices under different configurations and standards but mostly using legacy standards. A detailed AP power consumption analysis, exploring the whole set of degrees of freedom and capabilities of these devices is lacking in the state of the art. In this paper, we present a thorough power consumption analysis, covering the configuration options available in enterprise Wi-Fi APs from the three major vendors on the market. The goal is to understand how the power consumption of an AP varies with the different configurations, and provide insights on the parameters that significantly affect the AP power consumption. The obtained experimental results confirm previous state-of-the-art conclusions but contradict some of the studies and results found in the literature, while updating results and conclusions taken in the past to the most recent standards, configurations, and data rates available today. The analysis provided herein is a valuable source of information for deriving new AP power consumption models and designing energy-efficient Wi-Fi networks.