A key challenge for implementation of a circular economy model in manufacturing systems is the functional dependence of downstream processes on upstream byproducts. Design principles provide a framework for mapping goals to solutions by decomposing complex engineering problems into structured sets of requirements to be satisfied and embodied by design parameters and process variables. Large Language Models can computationally represent such textually-described design elements to quantify interconnections between problems, solutions, and processes. We present a Functional Digital Twin concept, powered by AI language modeling and guided by principles of manufacturing systems design, to identify functionally coupled process variables in an industrial symbiosis and automatically push alerts to stakeholders in a circular manufacturing system. Changes in byproduct composition are pushed downstream, and upstream decision-makers are guided to balance satisfying their design requirements with maintaining circularity of the system. The presented method is demonstrated in a case study of bio-based absorbent materials for intended use in disposable sanitary articles developed from byproducts of the agro-food industry.

Demographic change in Europe challenges companies as retiring employees take valuable expertise with them. To address this, knowledge graphs (KGs) are emerging as tools for structured knowledge representation. Simultaneously, large language models (LLMs) are increasingly being used as innovative solutions for information retrieval. However, LLMs generally process only public knowledge, and recent approaches integrating Retrieval Augmented Generation (RAG) for private knowledge retrieval often lack contextual relevance. To enhance trustworthiness and overcome these limitations, a method is proposed for embedding latent problem-solving structures within design processes into LLM-driven information retrieval systems. Using a case study in energy infrastructure, a KG of design problems was constructed by extracting functional requirements from semi-structured documentation via LLMs. This KG is further utilized by an LLM to answer queries, with results visualized through an interactive interface. Validation through field studies with engineers underscores the approach's effectiveness in enhancing contextual and trustworthy knowledge dissemination.

Background: A large proportion of individuals experience functional impairment in everyday life due to climate worry. However, the current understanding of this functional impairment is limited by the use of suboptimal measures. Furthermore, it is not known whether functional impairment due to climate worry affects pro-environmental behaviors (PEBs) or whether individuals who experience such impairment perceive a need for support. The aims of the current study were (1) to extend previous research using an established measure of functional impairment (the Work and Social Adjustment Scale, WSAS), (2) to explore the associations between climate worry, functional impairment, and PEBs, and (3) to describe the characteristics and the perceived need for support of individuals with functional impairment due to climate worry.

Methods: A cross-sectional survey targeting adult individuals who experience climate worry. Participants were recruited nationally in Sweden between September and October 2022. The survey included measures of climate worry severity, climate worry frequency, functional impairment, PEBs, depressive symptoms, sleep problems, and questions related to perceived need for support.

Results: A total of 1221 adults (75% women, mean age 46.3 years) were included in the analyses. Multivariate structural equation modeling revealed that climate worry severity and frequency were significantly associated with PEBs (β = 0.34 and β = 0.45, respectively). Climate worry frequency was associated with functional impairment (β = 0.41). Functional impairment was only marginally associated with PEBs (β = 0.05). Approximately 40% of the sample (n = 484) reported a high frequency and high severity of climate worry. Among these, one-third (n = 153) scored above the cutoff for significant impairment on the WSAS. Individuals in this group (high severity and frequency of climate worry as well as significant functional impairment) were more likely to experience depressed mood and sleep problems and were more interested in receiving support, specifically concerning strategies for worry management and sustainable behavior change.

Conclusions: Using an established measure of functional impairment, we found an association of climate worry with functional impairment and PEBs. Importantly, as there is a perceived need for support in individuals with impairment due to climate worry, interventions targeting this specific subgroup should be developed.

With each IPCC report, the science basis around climate change increases extensively in terms of scope, depth, and complexity. In converting this knowledge into societal climate action, research organisations face the challenge of reforming the ways they structure themselves, generate solutions, and communicate scientific findings to stakeholders. Here we present a mission-driven approach to guide those efforts.

Current digitalized manufacturing systems do not yet achieve the goal of smart manufacturing: precise con-trol and agility under unexpected disruptions. Push-Pull Digital Thread is a solution concept to enable contex-tual data and knowledge exchange across operational and functional units in a manufacturing enterprise. The extraction of decision reasoning and functional information can be facilitated by Large Language Models proc-essing information obtained from a decision maker at the point of decision. This concept shows a potential to address critical limitations in previous endeavours for smart manufacturing systems by building a semanti-cally searchable and sharable knowledge base in manufacturing systems and beyond.