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Developing a maturity model for circular economy in SME
No full textThe importance of implementing a circular economy in industry is constantly increasing and poses a variety of challenges, especially for SMEs. Maturity models help structuring the development of organizations by making trend topics more tangible and are, therefore, suitable for supporting the implementation of a circular economy. However, the existing circular economy maturity models lack specific recommendations for increasing the maturity level and do not consider the specific characteristics of SMEs. To address this gap, this paper presents the development path of a maturity model for a circular economy that is intended to provide holistic support for SMEs in the transition to a circular economy
Remodeling Learning Factories for Circular Economy
No full textThe Circular Economy is increasingly recognized as a critical approach to addressing the challenges of resource constraints and environmental sustainability in manufacturing. Despite this recognition, the practical integration of circular principles into manufacturing processes remains limited, partly due to a lack of required employee competences and skills. Learning Factories have emerged as an effective learning environment for competence development, providing realistic products and processes to teach methods and demonstrate technologies for value creation. This study aims to identify key fields of action for advancing Learning Factory concepts specifically for circularity through a two-stage survey conducted with Learning Factory operators. The findings reveal that Learning Factory design should prioritize the dimensions of process and product, with a focus on Remanufacturing operations. Furthermore, the results suggest that interdisciplinary aspects of the topic CE should be incorporated and a continuous best-practice exchange with other Learning Factory operators is crucial for the holistic remodeling of existing Learning Factories for circular manufacturing. These insights offer a foundation for future research on remolding of Learning Factories for ultimately supporting the transition to circular value creation in manufacturing
Machine learning enhanced time-resolved multi-particle tracking velocimetry in solid fuel particle group combustion
No full textParticle velocity is an essential parameter in solid fuel combustion studies, however, the accurate detection and tracking of particles in high particle number density (PND) combustion scenario remain challenging. The current study advances the machine-learning particle detection approaches for precise velocity measurements of solid particles. For visualizing particle locations, time-resolved laser Mie scattering experiments were performed for high-volatile bituminous (hvb) coal particles of different size burning in a high-temperature oxidizing laminar flow. The machine learning (ML) based object detection models you only look once (YOLO) and realtime detection transformer (RT-DETR) were trained on the conventional blob detection annotations (weak-label) from low-PND cases and evaluated against the manually labeled images from high-PND cases, which served as ground truth. Particle tracking was then performed using the simple online realtime tracking (SORT) algorithm. The results demonstrate that models trained on a limited set of weak-label data can achieve satisfactory prediction performance in complex environments that are difficult for traditional object detection methods. Slicing aided hyper inference (SAHI) algorithm is implemented for improving the performance of the used ML models. By evaluating the velocity statistics, it is found that the mean particle velocity decreases with increasing PND and particle size, primarily due to stronger particle–gas and particle–particle interactions. The particle dynamics are closely related to the position of volatile combustion zone
Fuzzy logic process control approach for intelligent parts drying processes
No full textIndustrial parts drying processes are essential elements of many process chains in the metal-working industry. Nevertheless, they are often
designed based on experience and lack a control strategy, leading to over- or under-drying. The following paper proposes a fuzzy-logic-based
approach for controlling the drying result, while also considering the resulting power consumption of the drying system. The developed fuzzy
logic controller is implemented to the convective drying system of a throughput cleaning machine. The study concludes that fuzzy logic
provides an effective means of controlling industrial parts drying processes for efficiently achieving targeted dryness levels, while providing
sufficient interpretability
Arthropod species loss underpins biomass declines
No full textRecent declines in arthropod diversity, abundance and biomass are central to the global biodiversity crisis. Yet, we lack a mechanistic understanding of the respective contributions of species richness, species identity and abundance to overall biomass change, and how the environment filters these processes. Synthesizing 11 years of data from a biodiversity experiment and from farmed grasslands in central Europe across a gradient of plant species richness and land-use intensity, we show that local arthropod biomass declines were predominantly (>90%) linked to species richness losses. Abundance declines among persisting species accounted for only 5–8% of lost biomass. The role of species identity depended on the environment and diminished over time: especially under high plant diversity and low land-use intensity, arthropod species with both below-average total biomass and above-average individual biomass (large, rare species) contributed disproportionately to species turnover—but this was only detectable in early years when the communities were still relatively abundant. We conclude that arthropod communities are currently homogenizing towards few common species of similar biomass, probably reducing their adaptability to future environmental change. Increasing the diversity and reducing the land-use intensity of grasslands may mitigate ongoing community simplification and loss of arthropod diversity and functioning
From Search to Dialogue: An Experimental Comparison of User Experience, Satisfaction and Success with ChatGPT and Google
No full textFabrication of sustainable 3D printed anisotropic bonded magnets using recycled Nd–Fe–B powder and low CO 2 footprint polyamide 12
No full textPolymer‐bonded Nd–Fe–B magnets, made from hard magnetic powder and a polymer binder, are essential in many high‐tech applications. The growing demand in energy‐conversion devices calls for a more circular and versatile approach to their production. This study presents a sustainable approach to fabricate anisotropic polymer‐bonded Nd–Fe–B magnets using recycled powder from end‐of‐life (EOL) hot‐deformed magnets. Employing laser powder bed fusion with a low CO2 footprint polyamide 12 matrix in combination with magnetic powder enables production of complex geometries. Two methods are compared for converting EOL hot‐deformed magnets into powder and the resulting performance of printed bonded magnets with these powders. Both powders have an elongated shape with the magnetic easy axis oriented perpendicular to the particle's length. Utilizing these anisotropic powders, based on a previously studied alignment mechanism, anisotropic bonded magnets are fabricated with over 60% higher magnetic performance compared to those made from EOL sintered magnet powders in 3D printing. The fabricated magnets have a remanence of 0.34 T and coercivity of 1238 kA m−1 . The findings demonstrate a pathway toward turning parts of the magnet market into a more circular economy by reducing reliance on primary Nd–Fe–B sources and enhancing efficiency of magnetic powder use
How Stakeholders Perceive Generative AI in Sustainability Reports: Assistance or Interference?
No full textProgressive improvements in Generative Artificial Intelligence (GenAI) are leading to an expansion of its application, including sustainability reporting. While this promises efficiency gains, little is known about how stakeholders perceive AI-generated reports. This study investigates the perception of external stakeholders of such reports with different levels of AI involvement, examined through an online experiment with 96 participants in Germany. Our findings show that human post-processing plays a crucial role: reports co-created with AI are perceived as equally credible as human-written ones, while fully automated reports are rated significantly lower. These results underline the relevance of human involvement for maintaining credibility in sensitive communication contexts. They also provide practical insights for communicating AI-assisted sustainability reporting, highlighting the psychological dynamics that shape stakeholder trust
It’s a match! Aligning employer branding and corporate real estate management
No full textPurpose
This study aims to provide insights into using corporate real estate (CRE) to enhance employer branding success. Therefore, different employee groups are identified according to their social, economic and ecological perception and assessment of CRE as well as their workplace-related needs.
Design/methodology/approach
Principal component analysis, hierarchical cluster analysis and analysis of variance are applied using cross-sectional data from n = 937 German office workers.
Findings
The clustering results reveal a cluster differentiation according to the social, economic and ecological CRE dimensions. Furthermore, examining workplace-related needs reveals high importance of productivity-enhancing attributes for the social cluster, status-related attributes for the economic cluster and attributes related to biophilic design for the ecological cluster. Aligning employees’ needs with workplace attributes offered can strengthen the employer branding success.
Originality/value
The study adds an interdisciplinary approach to previous research on the relationship between CRE and employer branding, incorporating both corporate real estate management (CREM) and human resource management (HRM). Insights from this study can support CREM in creating a fit between (potential) employees and the workplace-related needs to contribute to employer branding success
Transforming Scholarly Landscapes: The Influence of Large Language Models on Academic Fields beyond Computer Science
No full textLarge Language Models (LLMs) have ushered in a transformative era in Natural Language Processing (NLP), reshaping research and extending NLP's influence to other fields of study. However, there is little to no work examining the degree to which LLMs influence other research fields. This work empirically and systematically examines the influence and use of LLMs in fields beyond NLP. We curate LLMs and analyze papers citing LLMs to quantify their influence and reveal trends in their usage patterns. Our analysis reveals not only the increasing prevalence of LLMs in non-CS fields but also the disparities in their usage, with some fields utilizing them more frequently than others since 2018, notably Linguistics and Engineering together accounting for of LLM citations. Our findings further indicate that most of these fields predominantly employ task-agnostic LLMs, proficient in zero or few-shot learning without requiring further fine-tuning, to address their domain-specific problems. This study sheds light on the cross-disciplinary impact of NLP through LLMs, providing a better understanding of the opportunities and challenges