Abstract
The success of the Emergency Plan depends on the ability of its occupants to respond. For this reason, it is fundamental to develop an appropriate training strategy for each organization. This pilot study aimed to understand the influence of specific training program on the emergency response. This study included a total of twenty-two workers of a company. The workers were divided into three emergency response teams with four elements and one another group with ten elements. The emergency response team had specific training actions with theoretical and practical contents. Finally, all workers participated in an activity called emergency scenarios, where a moment of brainstorming was provided for the solve each scenario. The classifications obtained in different assessments moments (M1: after training and M2: after three weeks of training) revealed that knowledge had been acquired by participants. Additionally, it was verified that teams, with specific training, presented better results in their specific scenario. The emergency response training may have better results if it enhances teamwork and the involvement of all stakeholders. © Springer Nature Switzerland AG 2019.

Abstract
Microscopic simulation requires accurate car-following models so that they can properly emulate real-world traffic. In order to define these models, calibration procedures can be used. The main problem with reliable calibration methods is their high cost, either in terms of the time they need to produce a model or due to high resource requirements. In this paper, we examine a method based on virtual driving simulation to calibrate the Krauß car-following model by coupling the Unity 3D game engine with SUMO. In addition, we present a means based on the fundamental diagrams of traffic flow for validating the instances of the model obtained from the calibration. The results show that our method is capable of producing instances with parameters close to those found in the literature. We conclude that this method is a promising, cost-efficient calibration technique for the Krauß model. Further investigation will be required to define a more general approach to calibrate a broader range of car-following models and to improve their accuracy. © 2019 EUROSIS-ETI.

Abstract
This paper proposes a methodology to detect early signs of Parkinson's disease (PD) through free-speech in uncontrolled background conditions. The early detection mechanism uses signal and speech processing techniques integrated with machine learning algorithms. Three distinct speech databases containing patients' recordings at different stages of the PD are used for estimation of the parameters during the training and evaluation stages. The results reveal the potential in using Random Forest (RF) or Support Vector Machine (SVM) techniques. Once tuned, these algorithms provide a reliable computational method for estimating the presence of PD with a very high accuracy.

Abstract
The interaction between gold sub-nanometer clusters composed of ten atoms (Au10) and tetrakis(4-sulfonatophenyl)porphyrin (TPPS) was investigated through various spectroscopic techniques. Under mild acidic conditions, the formation, in aqueous solutions, of nanohybrid assemblies of porphyrin J-aggregates and Au10 cluster nanoparticles was observed. This supramolecular system tends to spontaneously cover glass substrates with a co-deposit of gold nanoclusters and porphyrin nanoaggregates, which exhibit circular dichroism (CD) spectra reflecting the enantiomorphism of histidine used as capping and reducing agent. The morphology of nanohybrid assemblies onto a glass surface was revealed by atomic force microscopy (AFM), and showed the concomitant presence of gold nanoparticles with an average size of 130 nm and porphyrin J-aggregates with lengths spanning from 100 to 1000 nm. Surface-enhanced Raman scattering (SERS) was observed for the nanohybrid assemblies.

Abstract
Pose invariant object detection and classification plays a critical role in robust image recognition systems and can be applied in a multitude of applications, ranging from simple monitoring to advanced tracking. This paper analyzes the usage of the Bag of Words model for recognizing objects in different scales, orientations and perspective views within cluttered environments. The recognition system relies on image analysis techniques, such as feature detection, description and clustering along with machine learning classifiers. For pinpointing the location of the target object, it is proposed a multiscale sliding window approach followed by a dynamic thresholding segmentation. The recognition system was tested with several configurations of feature detectors, descriptors and classifiers and achieved an accuracy of 87% when recognizing cars from an annotated dataset with 177 training images and 177 testing images. © Springer International Publishing AG 2018.

Abstract
Wine counterfeiting is a major problem worldwide. Within this context, an approach to the problem of discerning original wine bottles from forged ones is the use of natural features present in the product, object and/or material (using it "as is"). The proposed application uses the cork stopper as a unique fingerprint, combined with state of the art image processing techniques to achieve individual object recognition and smartphones as the authentication equipment. The anti-counterfeiting scheme is divided into two phases: an enrollment phase, where every bottle is registered in a database using a photo of its cork stopper inside the bottle; and a verification phase, where an end-user/retailer captures a photo of the cork stopper using a regular smartphone, compares the photo with the previously-stored one and retrieves it if the wine bottle was previously registered. To evaluate the performance of the proposed application, two datasets of natural/agglomerate cork stoppers were built, totaling 1000 photos. The worst case results show a 100% precision ratio, an accuracy of 99.94% and a recall of 94.00%, using different smartphones. The perfect score in precision is a promising result, proving that this system can be applied to the prevention of wine counterfeiting and consumer/retailer security when purchasing a wine bottle.