Abstract
Effective communication in engineering projects is pivotal for empowering the green transition, as it fosters multidisciplinary collaboration, ensures clarity across diverse stakeholders, and bridges technical and cultural gaps, ultimately driving sustainable innovation and project success. The main aim of this study is to give a contribution to overcome these communication limitations. This research explores the critical role of communication in engineering projects related to the green transition, as part of the ECO-GT project in Portugal. Through focus groups and interviews with different stakeholders, including engineers, product manufacturers and end-users, the research identifies communication challenges and essential skills required during project implementation. The findings show that the importance of multidisciplinary collaboration, adapted language depending on the target audience, and openness to feedback are essential to achieving project goals. Key findings include the need for tailored communication strategies at all project stages to overcome technical and cultural barriers. This research highlights the value of integrating communication training into engineering education to prepare future engineers for the complexities of green transition projects.

Abstract
This paper presents an optimisation approach based on mixed-integer programming for tactical planning decisions within fresh fruit processing industries. It applies to fruits such as oranges, tomatoes, guavas and others, where diluted fruit juice needs to be concentrated in evaporators to produce semi-finished or finished products. It considers agricultural and industrial activities, integrating them to address complex and interconnected decisions. Agricultural tasks include planting, harvesting, and transporting fruits from fields to processing plants, while industrial activities involve the production, inventory, and transportation of semi-finished and final products. This approach accommodates multiple agricultural regions, fruit varieties, processing plants, and products, operating on a weekly basis within a one-year planning horizon. It offers a detailed solution for harvesting, the fruit juice concentration process, inventory management for the products produced, and transportation of raw materials and products among processing plants. Production of semi-finished products is modelled using the Proportional Lot-Sizing and Scheduling Problem and the production of finished products is modelled adopting a blending lot-sizing problem. The results were validated through computational experiments using a dataset from a company that processes tomatoes and guavas. Scenario analyses were conducted to evaluate the solution's consistency and real-world applicability. The findings indicate that the approach can support decision making in practice, highlighting its potential as a valuable managerial, analytical, and optimisation tool for some agri-food industries. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

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Abstract
CoffeeMush is an innovative and sustainable project developed as part of the European Project Semester (EPS) at ISEP in 2024. This student project aims to tackle waste management environmental problems by turning coffee waste into mushrooms, a valuable food source. CoffeeMush consists of a smart device providing optimal conditions for mushroom cultivation, complemented by a user-friendly Android application for remote monitoring and control. The design was guided by ethical, sustainability, market and technical considerations. The paper describes the theoretical background of the project, the technical design, and the prototype development and testing. The results show the feasibility of CoffeeMush as a practical and environmentally friendly solution for urban mushroom cultivation, and its impact on sustainable food production and waste reduction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Abstract
The growing integration of intermittent renewable energy sources poses new challenges to power system stability [...]

Abstract
With the growing interest in underwater exploration and monitoring, autonomous underwater vehicles have become essential. The recent interest in onboard deep learning (DL) has advanced real-time environmental interaction capabilities relying on efficient and accurate vision-based DL models. However, the predominant use of sonar in underwater environments, characterized by limited training data and inherent noise, poses challenges to model robustness. This autonomy improvement raises safety concerns for deploying such models during underwater operations, potentially leading to hazardous situations. This article aims to provide the first comprehensive overview of sonar-based DL under the scope of robustness. It studies sonar-based DL perception task models, such as classification, object detection, segmentation, and simultaneous localization and mapping. Furthermore, this article systematizes sonar-based state-of-the-art data sets, simulators, and robustness methods, such as neural network verification, out-of-distribution, and adversarial attacks. This article highlights the lack of robustness in sonar-based DL research and suggests future research pathways, notably establishing a baseline sonar-based data set and bridging the simulation-to-reality gap.