Abstract
One of the most significant challenges for machine learning nowadays is the discovery of causal relationships from data. This causal discovery is commonly performed using Bayesian like algorithms. However, more recently, more and more causal discovery algorithms have appeared that do not fall into this category. In this paper, we present a new algorithm that explores global causal association rules with Uncertainty Coefficient. Our algorithm, CRPA-UC, is a global structure discovery approach that combines the advantages of association mining with causal discovery and can be applied to binary and non-binary discrete data. This approach was compared to the PC algorithm using several well-known data sets, using several metrics.

Abstract
An airborne wind energy system (AWES) can harvest stronger wind streams at higher altitudes which are not accessible to conventional wind turbines. The operation of AWES requires a controller for the tethered aircraft/kite module (KM), as well as a controller for the ground station module (GSM). The literature regarding the control of AWES mostly focuses on the trajectory tracking of the KM. However, an advanced control of the GSM is also key to the successful operation of an AWES. In this paper we propose a cascaded control strategy for the GSM of an AWES during the traction or power generation phase. The GSM comprises a winch and a three-phase induction machine (IM), which acts as a generator. In the outer control-loop, an integral sliding mode control (SMC) algorithm is designed to keep the winch velocity at the prescribed level. A detailed stability analysis is also presented for the existence of the SMC for the perturbed winch system. The rotor flux-based field oriented control (RFOC) of the IM constitutes the inner control-loop. Due to the sophisticated RFOC, the decoupled and instantaneous control of torque and rotor flux is made possible using decentralized proportional integral (PI) controllers. The unknown states required to design RFOC are estimated using a discrete time Kalman filter (DKF), which is based on the quasi-linear model of the IM. The designed GSM controller is integrated with an already developed KM, and the AWES is simulated using MATLAB and Simulink. The simulation study shows that the GSM control system exhibits appropriate performance even in the presence of the wind gusts, which account for the external disturbance.

Abstract
The advancement of technology and techniques applied to robotics contributes to increasing the quality of life and safety of humanity. One of the most widespread applications of mobile robotics is related to monitoring indoor environments. However, due to factors such as the size of the environment impacting the monitoring response, battery autonomy, and autonomous navigation in environments with unknown obstacles, they are still significant challenges in the diffusion of mobile robotics in these areas. Strategy adopting multiple robots can overcome these challenges. This work presents an approach to use multi-robots in hazardous environments with gas leakage to perform spatial mapping of the gas concentration. Obstacles arranged in the environment are unknown to robots, then a fuzzy control approach is used to avoid the collision. As a result of this paper, spatial mapping of an indoor environment was carried out with multi-robots that reactively react to unknown obstacles considering a point gas leak with Gaussian dispersion.

Abstract
Rehabilitation, restoration and maintenance of monuments, heritage and buildings pose challenging tasks to engineers and architects, as any intervention must respect their architectural and constructive characteristics. Often these are unknown and sources of information have long been lost in time. Thus, there is a need to use methods capable of providing information on a wide range of aspects such as building foundations, construction characteristics and materials, alterations from the original layout, infrastructure mapping, pathologies, etc. These methods must respect the inherent structural delicacy and characteristics of ancient buildings and non-destructive methods, NDT such as geophysical methods, have been proposed to investigate these problems. It is common knowledge that a single geophysical method cannot provide full information on the problems to investigate. Thus, herein the combined use of Seismic Transmission Tomography and Ground Penetrating Radar - GPR - is demonstrated to provide important results in the investigation of the constructive elements (columns and walls) of a 14th century UNESCO monument. As demonstrated, high-resolution geophysical data obtained from both methods provide very good images of the interior of both walls and columns giving information on the quality and spatial distribution of the materials used in the construction of the monument. Finally, the results herein discussed prove the suitability and complementarity of these two methods to investigate, built heritage, monuments and buildings in general.

Abstract

Abstract
Nas últimas décadas a utilização da inteligência artificial tem sido frequente no desenvolvimento de aplicações computacionais. Mais recentemente a aprendizagem automática, especialmente pelo uso da aprendizagem profunda (deep learning), tem impulsionado o crescimento e ampliado o desenvolvimento de sistemas inteligentes para diferentes domínios. No cenário atual de crescimento tecnológico estão a surgir com maior frequência os sistemas de recomendação (recommender systems) com diferentes técnicas para a filtragem de informações em grandes bases de dados. Um desafio é prover a recomendação adaptativa para mitigar a sobrecarga de informações em ambientes on-line. Este artigo revisa trabalhos anteriores e aborda alguns dos aspectos teórico-conceptuais e teórico-práticos que constituem os sistemas de recomendação, caracterizando o emprego de redes neuronais profundas (Deep Neural Network – DNN) para prover a recomendação sequencial apoiada pela recomendação baseada em sessão.;In recent decades, artificial intelligence use has been frequent in the computational applications development. More recently, machine learning, especially through the use of deep learning, has driven growth and expanded the intelligent systems development for different domains. In the current scenario of technological growth, the recommender systems appear with increasing frequency through their different techniques for information filtering in large datasets. It is a challenge to provide adaptive recommendation to mitigate information overload in online environments. This article reviews previous works and addresses some of the theoretical-conceptual and theoretical-practical aspects that constitute the recommender systems, characterizing the use of deep neural network (DNN) to provide sequential recommendation supported by session-based recommendation.