Abstract
The automotive industry faces major megatrends such as climate change and emissions control, digital transformation, and increased customer power, resulting in more intensive competition, and higher sophisticated vehicles. The application of QFD (Quality Function Deployment) can be particularly valuable to link customer expectations with the technical characteristics of the product. In the case of products, such as batteries for electric vehicles, where technology is not yet mature, and the technical requirements (e.g., autonomy) are continuously more demanding, this is particularly relevant. The QFD customer-oriented product development technique is applied to a cover of a battery pack, to improve the negotiation process with the car manufacturer, the automotive industry battery components supplier company and its suppliers, to ensure market success once the product is released. The application of the HoQ revealed that Product Design and Tolerancing are the main technical requirements with the most impact over the battery cover development, followed the Leakage ratio. This research confirms that the voice of the customer could be quite generic, and it is critical that these requirements are translated into engineering requirements, which, in turn, can be translated into items that can be measured quantitatively and actionable within the company. The application of the affinity diagram was found to be quite valuable to address the significant amount of subjective information, and it is also relevant that OEMs have a desire to standardize the electric vehicle platforms at least on fewer and general sizes, hinting the need for more collaborative team approaches. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.

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This paper presents an innovative modular autonomous underwater vehicle (MARA) developed for the exploration of underwater confined spaces such as underwater caves, flooded underground mines or complex tight infrastructures in underwater environments. The particular mission scenario of exploration of flooded underground mines was used as a key driver for the robot development. The autonomous underwater vehicle is described from the mechanical, hardware and software points of view. The availability of the INESC TEC underwater systems test tank and access conditions to Porto harbour and the Urgeirica mine allows for easy robot field validation. Preliminary results are also presented and discussed.

Abstract
Aprender a programar geralmente é um desafio para os estudantes: cada aluno possui suas dificuldades individuais e os professores precisam identificá-las para dar o suporte necessário a seus alunos. Neste sentido, este artigo apresenta parte dos resultados obtidos em uma Revisão Sistemática de Literatura sobre o processo de aprendizagem de algoritmos e programação. O principal objetivo do trabalho aqui apresentado é apontar as principais dificuldades e desafios vividos pelos alunos do nível superior em disciplinas de Algoritmos e programação, bem como o que estas dificuldades podem acarretar aos alunos ao longo do seu percurso acadêmico. A escolha pelo método da pesquisa se deu pelo fato de que a Revisão Sistemática de Literatura apresenta um tipo de estudo cuja metodologia é bem definida, sendo capaz de avaliar a pesquisa de maneira criteriosa, confiável e objetiva. Para este estudo, a revisão foi realizada em quatro bases de dados: Web of Science, Scopus, IEEE Xplore e RCAAP. Para tanto, foi definida uma questão de revisão, as palavras-chave, a equação necessária ao estudo, bem como o protocolo de revisão. Inicialmente foram encontrados 350 artigos, dos quais 169 artigos foram pré-selecionados. Após a aplicação dos critérios de inclusão e exclusão, 33 artigos foram selecionados para a análise e desenvolvimento deste trabalho. Com a análise, pudemos verificar e categorizar as dificuldades e desafios enfrentados pelos estudantes durante o processo de aprendizagem de algoritmos e programação. Com os resultados apresentados, este artigo pode contribuir com uma visão geral capaz de servir de ponto de partida para outras pesquisas, bem como para auxiliar docentes e estudantes na percepção destas dificuldades.

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The integrated use of electricity and natural gas has captured great attention over recent years, mainly due to the high efficiency and economic considerations. According to the energy hub design and operation, which allows using different energy carriers, it has turned into a critical topic. This paper develops a two-stage stochastic model for energy hub planning and operation. The uncertainties of the problem have arisen from the electric, heating, and cooling load demand forecasts, besides the intermittent output of the solar photovoltaic (PV) system. The scenarios of the uncertain parameters are generated using the Monte-Carlo simulation (MCS), and the backward scenario reduction technique is used to alleviate the number of generated scenarios. Furthermore, this paper investigates the effectiveness of demand response programs (DRPs). The presented model includes two stages, where at the first stage, the optimal energy hub design is carried out utilizing the particle swarm optimization (PSO) algorithm. In this respect, the capacity of the candidate assets has been considered continuous, enabling the planning entity to precisely design the energy hub. The problem of the optimal energy hub operation is introduced at the second stage of the model formulated as mixed-integer non-linear programming (MINLP). The proposed framework is simulated using a typical energy hub to verify its effectiveness and efficiency.

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