Abstract

Abstract
The robot presented in this paper was developed with the main focus on participating in robotic competitions. Therefore, the subsystems here presented were developed taking into account performance criteria instead of simplicity. Nonetheless, this paper also presents background knowledge in some basic concepts regarding robot localization, navigation, color identification and control, all of which are key for a more competitive robot.

Abstract
Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot's operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects.

Abstract
O artigo apresenta o percurso do qual emergiu a pesquisa A CIDADE COMO ESPAÇO DE APRENDIZAGEM e, um dos primeiros territórios que se configurou o projeto de aprendizagem denominado Da cultura ao turismo: o patrimônio de Bom Princípio, desenvolvido por estudantes de sexto ano de uma escola pública do município de Bom Princípio – RS.  O problema que deu origem à pesquisa é assim expresso: Como os games e a gamificação podem contribuir para a construção de situações de aprendizagem e práticas pedagógicas que se apropriem da cidade como espaço para promover a educação cidadã, expressando um novo tipo de ecologia social, pós-urbana e atópica? O objetivo principal consistiu em compreender a contribuição dos games e da gamificação para o desenvolvimento de metodologias e práticas pedagógicas que possibilitassem transcender o espaço físico das escolas de educação básica para a cidade, tendo em vista a educação cidadã. Para o desenvolvimento da pesquisa, nos apropriamos do método cartográfico de pesquisa-intervenção. Como resultado, a pesquisa forneceu elementos que nos permitiram compreender que a partir da problematização do tempo presente, a apropriação de elementos de jogos, suportados pela aprendizagem inventiva, o reconhecimento atento da/pela cidade, transcendendo, dessa forma, o espaço físico da escola para a cidade, a qual para além de se constituir no espaço geográfico, se amplia no digital. Isso provocou uma ampliação na compreensão do habitar, do ensinar e do aprender, potencializando a Educação Cidadã. Esses elementos contribuíram ainda para a sistematização do que Schlemmer (2018) denominou metodologia inventiva Projeto de Aprendizagem Gamificado.

Abstract
The continuous evolution of programming languages has brought benefits and new challenges for software developers. In recent years, we have witnessed a rapid release of new versions of mainstream programming languages like Java. While these advancements promise better security, enhanced performance, and increased developers' productivity, the constant release of new language versions has posed a particular challenge for practitioners: how to keep their systems up-to-date with new language releases. This thesis aims to understand the pains, motivations, and practices developers follow during rejuvenating efforts-a particular kind of software maintenance whose goal is to avoid obsolesce due to the evolution of programming languages. To this end, we are building and validating a theory using a mixed methods study. In the first study, we interviewed 23 software developers and used the Constructivist Grounded Theory Method to identify recurrent challenges and practices used in rejuvenation efforts. In the second study, we mined the software repositories of open-source projects written in C++ and JavaScript to identify the adoption of new language features and whether or not software developers conduct large rejuvenation efforts. The first study highlights the benefits of new feature adoption and rejuvenation, revealing developer methods and challenges. The second study emphasizes open-source adoption trends and patterns for modern features. In the third and final study, our goal is to share our theory on software rejuvenation with practitioners through the Focus Group method with industrial patterns.

Abstract
Optimising operations in bulk cargo ports is of great relevance due to their major participation in international trade. In inbound operations, which are critical to meet due dates, the product typically arrives by train and must be transferred to the stockyard. This process requires several machines and is subject to frequent disruptions leading to uncertain processing times. This work focuses on the scheduling problem of unloading the wagons to the stockyard, approaching both the deterministic and the stochastic versions. For the deterministic problem, we compare three solution approaches: a Mixed Integer Programming model, a Constraint Programming model and a Greedy Randomised algorithm. The selection rule of the latter is evolved by Genetic Programming. The stochastic version is tackled by dispatching rules, also evolved via Genetic Programming. The proposed approaches are validated using real data from a leading company in the mining sector. Results show that the new heuristic presents similar results to the company's algorithm in a considerably shorter computational time. Moreover, we perform extensive computational experiments to validate the methods on a wide spectrum of randomly generated instances. Finally, as managing uncertainty is fundamental for the effectiveness of these operations, distinct strategies are compared, ranging from purely predictive to completely reactive scheduling. We conclude that re-scheduling with high frequency is the best approach to avoid performance deterioration under schedule disruptions, and using the evolved dispatching rules incur fewer deviations from the original schedule.