The economic evaluation of circular business models in the early development phase is complicated due to novel business logic, high uncertainty, and incomplete data. Existing approaches are either overly simplified or require significant modelling effort. Based on the design science research methodology, we developed an AI-supported software tool that systematically converts business model information into a probabilistic simulation model. This approach combines a circular business model canvas with a dynamic Monte Carlo simulation to explicitly model uncertainties and present their effects on key financial indicators transparently. An evaluation with industry representatives revealed the approach's high perceived comprehensibility, applicability, and usefulness, underscoring its suitability for early decision-making phases in the circular economy.

Growing sustainability challenges require a shift from ex-post impact mitigation to sustainability by design, embedding environmental, economic, social, and operational considerations directly into early-stage product development. Decisions taken under uncertainty in early design phases largely determine long-term system performance, highlighting the need for structured decision support. Remanufacturing has gained attention as a promising circular economy strategy; however, its sustainability cannot be assumed a priori and depends on contextual and design-related factors. This paper presents the conceptual development of a sustainability screening framework to support early-stage remanufacturing decisions. Grounded in the Design Research Methodology and informed by a Systematic Literature Review, the study structures a three-stage screening logic that enables the systematic comparison of alternative remanufacturing strategies across four sustainability dimensions, benchmarked against conventional new manufacturing. A spreadsheet-based mock-up is provided to illustrate the operationalisation of the framework.

This study examines how asymmetric avatar roles influence player experience in a cooperative virtual reality puzzle game. Two players were required to collaborate to escape a virtual house, with one controlling a large, strong avatar and the other a small, agile one. Through timed gameplay, surveys, observations, and interviews, we investigated how differences in avatar size and strength shaped immersion, agency, communication, and engagement. The qualitative data showed that the two roles produced notably different experiences. The small avatar was consistently perceived as more engaging due to its mobility, exploratory possibilities, and unusual perspective, while the large avatar felt more restricted and less capable than expected. Role switching intensified players’ focus on comparing perspectives rather than attending to their partner, which reduced opportunities for empathy and mutual awareness. These findings highlight the importance of meaningful affordances, clear avatar visibility, and balanced role design when creating asymmetric collaborative VR experiences.

Calcium phosphate cement (CPC) is a resorbable, biocompatible implant material used to treat cranial defects. However, challenges in additive manufacturing limit the production of large, patient-specific, single-piece CPC lattice structures. This paper introduces a novel, reproducible, parametric process for designing modular neurocranial implants for patients aged three years and older. The process employs scalable Voronoi diagrams to divide the implants into central segments and edge segments while incorporating z-gap profiles and pore distributions to promote bone growth. Starting from an implant base body in nTop, a design process generates individual segments according to specified design requirements. A preprocessing workflow in the internally developed software SliceIt! prepares the modular implant for additive 3D printing. The approach was evaluated through twelve case studies and two prototypes, one for handling validation and one manufactured from CPC.