Abstract
Autonomous Unmanned Aerial Vehicles (UAVs) become an important field of research in which multiple applications can be designed, such as surveillance, deliveries, and others. Thus, studies aiming to improve the performance of these vehicles are being proposed: from new sensing solutions to more robust control techniques. Additionally, the autonomous UAV has challenges in flight stages as the landing. This procedure needs to be performed safely with a reduced error margin in static and dynamic targets. To solve this imperative issue, many applications with computer vision and control theory have been developed. Therefore, this paper presents an alternative method to train a multilayer perceptron neural network based on fuzzy Mamdani logic to control the landing of a UAV on an artificial marker. The advantage of this method is the reduction in computational complexity while maintaining the characteristics and intelligence of the fuzzy logic controller. Results are presented with simulation and real tests for static and dynamic landing spots. For the real experiments, a quadcopter with an onboard computer and ROS is used.

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
Luminescent metal-organic frameworks (LMOFs) are promising materials for lighting and sensing applications. Herein, exposure of the highly luminescent Zn-2(bpdc)(2)(bpee) MOF (H(2)bpdc = 4,4'-biphenyldicarboxylic acid and bpee = 1,2-bipyridylethene) to subppm amine contents turns on a new absorption band unambiguously ascribed to free bpee molecules concomitant with the gradual appearance of a new photoluminescence band at shorter wavelengths. These findings combined with Fourier-transform infrared spectra, powder X-ray diffraction and thermogravimetric analysis of exposed LMOF powders confirm that bpee ligands are exchanged by amines and released inside the LMOF, triggering absorption and luminescence features which can be exploited for highly sensitive amine recognition. This principle was demonstrated in mixed matrix membranes (MMMs) prepared by a simple solvent-free method consisting of mixing Zn-2(bpdc)(2)(bpee) with dimethylvinyl-terminated dimethylsiloxane and dimethylhydrogen siloxane. This method enabled the production of free-standing, permeable, and highly transparent MMMs which showed enormous potential and sensitivity to the detection of amines in gas phase and aqueous medium.