Fraunhofer Institute for Industrial Engineering

In the development of innovations over the past decades, data-driven methods have become commonplace. Alongside advantages in effectiveness and efficiency comes the danger of relying on data describing past rather than future developments which can result in a loss of imagination and creativity. In parallel, a growing awareness of the interface between innovation and science fiction can be observed in academia and among practitioners. This awareness is most often based on the hypothesis that taking inspiration from science fiction can create value in the development of innovations. Despite numerous case studies in the literature describing this interface, a major part of methods applied seem to have been chosen in an unstructured, almost random way. This study investigates the literature in search of science fiction-related methods able to support the development of innovations. With around 60.000 publications considered based on a high-level search, a refined search combined with a manual search led to 17 science fiction-related methods to support the development of innovations. Using a six-phase generic innovation process, the methods identified were mapped into the process based on semantic similarities between the objectives of each method and the objectives of innovation process phases. This mapping is understood as an overview and conceptualization offering a baseline for future academic research and guidance for practitioners on choosing the most appropriate science fiction-related methods for developing innovations. • Science fiction related methods for innovation mapped to a six-phase innovation process • Systematic literature review (PRISMA-inspired) across academia and grey literature • Practical selection guide for choosing science-fiction related methods by goal-to-phase fit • Tools for foresight and ideation: scenarios, wild cards, future personas, prototyping • Strong impact in the fuzzy front end: broadens ideas and reduces bias

Number-conserved subspace encoding reduces resources needed for quantum simulations, but scalable complexity trade-off bounds for M modes and N particles with O(NlogM) qubits have remained unknown. We study qubit-gate-measurement trade-offs through the lens of classical/quantum error correction complexity and develop a framework of fermionic gate and measurement complexity based on classical encoder/decoder appearing in the error correction framework. We demonstrate optimal encoding with random classical parity check code and propose the Fermionic Expectation Decoder for scalable probability decoding in O(M4) bases. The protocol is tested with variational quantum eigensolver on LiH in the STO-3G and 6-31G bases, and H2 potential energy curve in the 6-311G* basis.

Attention allows individuals to prioritize and effectively process relevant information while ignoring task-irrelevant distractions. It plays a critical role in task performance, learning and creativity. This study examines how varying levels of workl

Abstract. Accurate detection of on-street parking occupancy is crucial for urban traffic management. We present DroneVision, a low code solution that enables municipalities to analyze parking occupancy using drone-acquired aerial imagery and vision language models (VLM). This approach allows municipal authorities to upload commercial drone imagery and parking space information, automatically processes these inputs, and generates comprehensive analysis reports. We evaluated two state-of-the-art open-source VLMs against traditional pretrained convolutional neural networks (CNNs) for drone imagery analysis. Results demonstrate the effectiveness of our VLM-based approach, offering a scalable solution that can be integrated into smart city infrastructures while making advanced AI technology accessible to municipal authorities through a low-code interface.

This paper presents a newly created Product-Service-Systems reference framework supporting the integration of virtual commissioning and digital twins throughout the entire product service life cycle. This six-phase framework is ranging from target group definition to the integration of smart services into operational systems. Currently, Digital Transformation in the industry is driving increasingly efficient and intelligent production and services processes. This proposed reference framework is intended to facilitate the digital transformation process in SMEs in Germany, through the use of Digital Twins, for minimising errors, reducing commissioning time, and optimising system performance. Overall, this study demonstrates how this new approach would help SMEs optimizing their production processes while enhancing the quality and flexibility of their systems.

Transitioning from Industry 4.0 to Industry 5.0 signifies a significant change in how technology integrates with workplace dynamics. While Industry 4.0 focused on streamlining production through automation, Industry 5.0 centers on human-centric approaches. This entails designing work environments that prioritize human comfort and efficiency by incorporating technology that complements human capabilities. Collaborative robots, known as cobots, play a pivotal role in this shift, aiding humans in tasks while fostering increased human involvement. However, maximizing the benefits of cobots necessitates workspace designs that optimize both human and robotic resources’ needs and preferences. A promising strategy involves implementing a dynamic task allocation system. This approach employs a neural network to adaptively reallocate tasks to prevent any loss in performance. Such advancements represent a significant stride towards establishing production settings that prioritize the effectiveness of human workers.