Abstract
Through technological advents from Industry 4.0 and the Internet of Things, as well as new Big Data solutions, predictive maintenance begins to play a strategic role in the increasing operational performance of any industrial facility. Equipment failures can be very costly and have catastrophic consequences. In its basic concept, Predictive maintenance allows minimizing equipment faults or service disruptions, presenting promising cost savings. This paper presents a data-driven approach, based on multiple-instance learning, to predict malfunctions in End-of-Line Testing Systems through the extraction of operational logs, which, while not designed to predict failures, contains valid information regarding their operational mode over time. For the case study performed, a real-life dataset was used containing thousands of log messages, collected in a real automotive industry environment. The insights gained from mining this type of data will be shared in this paper, highlighting the main challenges and benefits, as well as good recommendations, and best practices for the appropriate usage of machine learning techniques and analytics tools that can be implemented in similar industrial environments. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Computational thinking is the thinking process involved in formulating problems to admit a computational solution. This article describes a study in which the code.org platform was used to develop computational thinking with Elementary school students. After proper introduction and contextualization, we describe the 198 students from 4th grade involved in the study, following the process of collecting and analyzing data from the code.org platform. We conclude with the evaluation carried out by the students. The main conclusion of this study is that code.org is a valid option for developing computational thinking with Elementary school students. Also, a reliable way for students to start solving real-life problems, stimulating the capacity for abstraction through simulated and experienced practice.

Abstract
The Internet of Things asserts that several applications, such as smart cities or intelligent agriculture, can be based on various embedded systems programmed to do different tasks, by transferring data over a network from sensors to a server, where the information is stored and treated, supporting the decision-making process. In this context, LoRaWAN is an accurate network topology based on a wireless technology called LoRa that is capable of transmitting small data rates at a long range, using low-powered devices, making it ideal for the acquisition of climate variables, such as temperature and relative humidity. Applying this architecture to agriculture buildings can be very useful to guarantee indoor thermal comfort conditions. In this study, this technology is applied to a passive solar system composed by a high thermal inertia wall, defined as Trombe wall, with air vents provided in the massive wall to improve heat transfer by air convection, and an external shading device to avoid overheating during summer and heat losses during winter. It is intended to analyze the possibility to control the interiortemperature of a poultry brooding house given that, in the early stages of life, chickens need accurate climate conditions in order to enhance their growth and reduce their mortality rate. In brief, temperature values acquired by different sensors placed on the Trombe wall travel through a LoRaWAN wireless network and are received by an application that controls the actuators, in this case, the opening and closing of the Trombe wall air vents, while the external shading device is controlled locally.

Abstract
The robotics field is widely used in the industrial domain, but nowadays several other domains could also take advantage of it. This interdisciplinary branch of engineering requires the use of human interfaces, efficient communication systems, high storage and processing capabilities, among other issues, to perform complex tasks. This paper aims to propose a cloud-based framework platform for robot operation in a hospital environment, addressing some challenges, such as communications security and processing/storage features. The recent developments in the artificial intelligence field and cloud resources sharing are allowing the penetration of robots in unstructured environments. However, some new challenges and solutions need to be tested in real environments. Our main contribution is to decrease the time-consumption related to processing and storage costs, associated with the physical processing resources of the robots. Also, the proposed methods provide an increase of the processing variables that are not yet present in the physical resources, such as in the case of robots with limited processing time or storage capabilities. This paper presents a platform based on Cloud Computing with services to support processing, storage and analytics applied to hospital environments. The proposed platform enables to achieve a decrease in the time-consumption, especially when it is intended to retrieve information about all robot activities. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
In this paper the comparison between three convolutional neural networks, used for the control of bio-inspired multi-robots in a simulated environment, is performed through manual gestures captured in real time by a webcam. The neural networks are: VGG19, GoogLeNet and Alexnet. For the training of networks and control of robots, six gestures were used, each gesture corresponding to one action, collective and individual actions were defined, the simulation contains four bio-inspired robots. In this work the performance of the networks in the classification of gestures to control robots is compared. They proved to be efficient in the classification and control of agents, with Alexnet achieving an accuracy of 98.33%, VGG19 98.06% e Googlelenet 96.94%.

Abstract
The Trombe wall is a passive solar system that can improve buildings energy efficiency. Despite the studies already developed in this field, more research is needed to assess the possibility of its integration in buildings avoiding user intervention. In this study, the influence of air vent management and materials' heat storage capacity upon its thermal performance, particularly in the temperature fluctuation and indoor conditions, was discussed. Comparing two days with similar solar radiation (SR) for non-ventilated (NVTW) and ventilated (VTW) Trombe walls, a differential of 43 degrees C between the external surface temperature and the one in the middle of the massive wall was verified for NVTW, while for VTW this value was 27 degrees C, reflecting the heat transfer by air convection, which reduced greenhouse effect, solar absorption and heat storage. A cooling capacity greater than 50% was verified for VTW compared to NVTW during night periods. An algorithm for the Trombe wall's automation and control was proposed considering SR as variable. Air vents and external shading devices should be open when SR exceeds 100 W/m(2) and closed for 50 W/m(2) to obtain at least 20 degrees C inside the room. Closing for 50 W/m(2) and opening for values lower that 20 W/m(2) is suggested for summer periods.