Abstract
This paper describes a mobile application, developed in an educational context, by the students of the Degree in Computer Engineering of the Instituto Politécnico de Bragança, allowing them to develop skills, based on real-world community problems solving, promoting by this way its engagement, and, at the same time, provide a solution to an effective need of the local community. The developed application has as goal to support the Human Veterinary Resources Management in a Low Density Population Context. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Several research works have applied Reinforcement Learning (RL) algorithms to solve the Rate Adaptation (RA) problem in Wi-Fi networks. The dynamic nature of the radio link requires the algorithms to be responsive to changes in link quality. Delays in the execution of the algorithm due to implementional details may be detrimental to its performance, which in turn may decrease network performance. These delays can be avoided to a certain extent. However, this aspect has been overlooked in the state of the art when using simulated environments, since the computational delays are not considered. In this paper, we present an analysis of computational delays and their impact on the performance of RL-based RA algorithms, and propose a methodology to incorporate the experimental computational delays of the algorithms from running in a specific target hardware, in a simulation environment. Our simulation results considering the real computational delays showed that these delays do, in fact, degrade the algorithm’s execution and training capabilities which, in the end, has a negative impact on network performance. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Abstract
The growth and modernization of the power system are the keys to enabling economic progress. The deregulation, added to the new emerging production technologies, conversion, and storage, triggered a change in the way of managing the power system worldwide. This work analyses the optimal dispatch of a virtual power plant (VPP) with active participation in the electricity market, considering multi-energy systems. The objective is to minimize the total operating cost of the power plant. The power plant is fed by two external networks: electrical and natural gas. The VPP is composed of energy production, conversion, and storage technologies, also considering the integration of a wind turbine and a set of electric vehicles (EVs). In addition to the Grid-to-Vehicle (G2V) charging, the advantage of Vehicle-to-Grid (V2G) technology is also verified, which allows the injection of power into the grid through the vehicles and Vehicle-to-Load (V2L) technology, enabling EVs to contribute to the satisfaction of the electrical load, reducing the costs, showing the advantages as well of EVs' integration in the VPP under analysis.

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To produce more food and tackle the labor scarcity, agriculture needs safer robots for repetitive and unsafe tasks (such as spraying). The interaction between humans and robots presents some challenges to ensure a certifiable safe collaboration between human-robot, a reliable system that does not damage goods and plants, in a context where the environment is mostly dynamic, due to the constant environment changes. A well-known solution to this problem is the implementation of real-time collision avoidance systems. This paper presents a global overview about state of the art methods implemented in the agricultural environment that ensure human-robot collaboration according to recognised industry standards. To complement are addressed the gaps and possible specifications that need to be clarified in future standards, taking into consideration the human-machine safety requirements for agricultural autonomous mobile robots.