The AMSHI project aims at contributing to reduce the urban heat island effect, by designing a self-locking paving block with a low degree of embodied energy for public spaces.
Industry & Innovation
The ‘Data-Driven Customer Value Systems’ project was conceived as a response to these pressing industry challenges. Our role encompassed a comprehensive examination of the Industry and Market, and the development familias & personas with a specific lens on the 18-30-year-old customer segment, to gain profound insights into customer behaviors, thereby establishing the foundation for innovative service proposals.
Our project addresses critical challenges in greenhouse cooling by aiming to reduce water consumption, improve sustainability, and enhance performance. Stakeholder analysis guided our approach, and research into materials and experimental activities led to promising alternatives. Our goal is to provide an innovative and sustainable solution that meets the diverse needs of stakeholders, ensures water conservation, and secures the economic viability of greenhouse applications. By focusing on these strategies and technologies, we can contribute to the advancement of sustainable practices in the agricultural sector while addressing the pressing issue of water scarcity.
Sanlorenzo’s MILDS project represents a significant step forward in leveraging Digital Twin technology to revolutionize the superyacht industry. By integrating data-driven maintenance, energy efficiency services, and a comprehensive DT infrastructure, the company aims to meet the demands of the emerging userrequirements, while delivering exceptional value to its customers.
The current landscape of Mixed Reality (MR) applications in Education finds common employment in rendering 3D content in digital spaces. We noticed an unexplored area in the development of 3D-based activities that, combined with 3D content, could lead to comprehensive teaching experiences. Our project, in partnership with Vodafone and FifthIngenium, aims to fill this gap.
Unlike traditional methods that search for the best model only considering the performance on the downstream task, NebulOS also takes into the energy consumption when training a model on a specific hardware, producing tailored designs on a variety of different devices. This ensures that the final model aligns with the end-user’s unique needs, resources, and preferences.