Abstract
In nature, and particularly in agriculture, pollination is fundamental for the sustainability of our society. In this context, pollination is a vital process underlying crop yield quality and is responsible for the biodiversity and the standards of the flora. Bees play a crucial role in natural pollination; however, their populations are declining. Robots can help maintain pollination levels while humans work to recover bee populations. Swarm robotics approaches appear promising for robotic pollination. This paper proposes the cooperation between multiple Unmanned Aerial Vehicles (UAVs) and an Unmanned Ground Vehicle (UGV), leveraging the advantages of collaborative work for pollination, referred to as Pollinationbots. Pollinationbots is based in swarm behaviors and methodologies to implement more effective pollination strategies, ensuring efficient pollination across various scenarios. The paper presents the architecture of the Pollinationbots system, which was evaluated using the Webots simulator, focusing on path planning and follower behavior. Preliminary simulation results indicate that this is a viable solution for robotic pollination. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Software businesses are continuously increasing their presence in the cloud. While cloud computing is not a new research topic, designing software for the cloud is still challenging, requiring engineers to invest in research to become proficient at working with it. Design patterns can be used to facilitate cloud adoption, as they provide valuable design knowledge and implementation guidelines for recurrent engineering problems. This work introduces a pattern language for designing software for the cloud. We believe developers can significantly reduce their R&D time by adopting these patterns to bootstrap their cloud architecture. The language comprises 10 patterns, organized into four categories: Automated Infrastructure Management, Orchestration and Supervision, Monitoring, and Discovery and Communication. © The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature 2025.

Abstract
People around the globe struggle with health issues requiring regular medication. Children, in particular, suffer more and more from chronic illnesses. In 2024, a team of six EPS@ISEP students chose to design a solution for this problem, followed by the assembly and testing of the corresponding proof-of-concept prototype. The aim was to design a solution for children to take the right medication, at the right time and in the right dose, in a pleasant and engaging way, based on technical, ethical, sustainability and market analyses. Focusing on children between the ages of 8 and 12, the team decided to incorporate a motivational system based on rewards to ensure that they take their medication correctly. The outcome is billy, a pill dispenser controlled via an app which allows carers to plan doses and release rewards, and children to autonomously take their medication. The system dispenses up to 21 doses of medication to the child through fingerprint authentication, and photographs the child taking the medication to reassure carers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Habitat loss, climate change, and pesticide use are key threats affecting beetle populations. This paper describes Scarabreed, a project that contributes to mitigate the beetle decline crisis. It was carried out by a team of six European students from different engineering fields and nationalities within the European Project Semester (EPS) at the Instituto Superior de Engenharia do Porto (ISEP), a project-based and teamwork learning framework. The designed solution – the Beetle Breeder Version 2 (BBV2) – consists of a smart modular vivarium created especially for beetle breeding. It monitors and controls relevant habitat parameters and offers two user-friendly interfaces (on-device and a Web application). The innovative modular structure of the vivarium allows easy scaling, customisation, and transportation. As a whole, the project offers significant environmental benefits: (i) facilitates the captive breeding of endangered beetle species, promoting population restoration efforts; (ii) fosters, as an educational tool, youth and general public awareness about the crucial role beetles play in ecosystems; and (iii) adopts eco-efficient and responsible business practices by following ethics and sustainability driven design and marketing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
The European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP) is a capstone engineering design program where students, organised in multidisciplinary and multicultural teams, create a solution for a proposed problem, bearing in mind ethical, sustainability and market concerns. The project proposals are usually aligned with the United Nations Sustainable Development Goals (SDG). New sustainable food production methods are essential to cope with the continuous population growth and aligned with SDG2 and SDG12. In this context, this paper describes the research and work done by a team of Erasmus students enrolled in EPS@ISEP during the spring of 2022. Since sustainable algae farming can be a suitable source of food, the team’s goal was the design and develop a proof-of-concept prototype, named GREEN·flow, of a symbiotic aquaponic system to farm algae and fish. The smart GREEN·flow concept comprises a modular structure and an app for control and supervision. The proposed design was driven by state-of-the-art research, targeted to a specific market niche based on a market analysis, and considering sustainability and ethics concerns, all of which are described in this manuscript. A proof-of-concept prototype was built and tested to verify that it worked as intended. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
This paper addresses a job shop scheduling problem with peak power constraints, in which jobs can be processed once or multiple times on either all or a subset of the machines. The latter characteristic provides additional flexibility, nowadays present in many manufacturing systems. The problem is complicated by the need to determine both the operation sequence and starting time as well as the speed at which machines process each operation. Due to the adherence to renewable energy production and its intermittent nature, manufacturing companies need to adopt power-flexible production schedules. The proposed power control strategies, that is, adjusting processing speed and timing to reduce peak power requirements may impact production time (makespan) and energy consumption. Therefore, we propose a bi-objective approach that minimizes both objectives. A linear programming model is developed to provide a formal statement of the problem, which is solved to optimality for small-sized instances. We also proposed a multi-objective biased random key genetic algorithm framework that evolves several populations in parallel. Computational experiments provide decision and policymakers with insights into the implications of imposing or negotiating power consumption limits. Finally, the several trade-off solutions obtained show that as the power limit is lowered, the makespan increases at an increasing rate and a similar trend is observed in energy consumption but only for very small makespan values. Furthermore, peak power demand reductions of about 25% have a limited impact on the minimum makespan value (4-6% increase), while at the same time allowing for a small reduction in energy consumption.