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.

Abstract
Self-localization of a robot is one of the most important requirements in mobile robotics. There are several approaches to providing localization data. The Ultra Wide Band Time of Flight provides position information but lacks the angle. Odometry data can be combined by using a data fusion algorithm. This paper addresses the application of data fusion algorithms based on odometry and Ultra Wide Band Time of Flight positioning using a Kalman filter that allows performing the data fusion task which outputs the position and orientation of the robot. The proposed solution, validated in a real developed platform can be applied in service and industrial robots.

Abstract
Indoor Air Quality (IAQ) describes the air quality of a room, and it refers to the health and comfort of the occupants. Typically, people spend around 90% of their time in indoor environments where the concentration of air pollutants and, occasionally, more than 100 times higher than outdoor levels. According to the World Health Organization (WHO), indoor air pollution is responsible for the death of 3.8 million people annually. It has been indicated that IAQ in residential areas or buildings is significantly affected by three primary factors, they are outdoor air quality, human activity in buildings, and building and construction materials. In this context, this work consists of a real-time IAQ system to monitor thermal comfort and gas concentration. The system has a data acquisition stage, captured by the WSN with a set of sensors that measures the data and send it to be stored on the InfluxDB database and displayed on Grafana. A Linear Regression (LR) algorithm was used to predict the behavior of the measured parameters, scoring up to 99.7% of precision. Thereafter, prediction data is stored on InfluxDB in a new database and displayed on Grafana. In this way, it is possible to monitor the actual measurement data and prediction data in real-time. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
We study a setting in which a company not only has a fleet of capacitated vehicles and drivers available to make deliveries but may also use the services of occasional drivers (ODs) willing to make deliveries using their own vehicles in return for a small fee. Under such a business model, a.k.a crowdshipping, the company seeks to make all the deliveries at the minimum total cost, i.e., the cost associated with their vehicles plus the compensation paid to the ODs.We consider a stochastic and dynamic last-mile delivery environment in which customer delivery orders, as well as ODs available for deliveries, arrive randomly throughout the day, within fixed time windows.We present a novel deep reinforcement learning (DRL) approach to the problem that can deal with large problem instances. We formulate the action selection problem as a mixed-integer optimization program.The DRL approach is compared against other optimization under uncertainty approaches, namely, sample -average approximation (SAA) and distributionally robust optimization (DRO). The results show the effective-ness of the DRL approach by examining out-of-sample performance.