Testing the Robustness of Circularity Indicators: Empirical Insights from Workshops on an Industrial Product

AbstractMonitoring properly the circularity performance of technical products is a point of increasing importance. Yet, evaluating the circularity potential of products during (re)design and development phases is a challenging task. In this study, several C-indicators are experienced by doctoral students and industrialists through two workshops on a real-world industrial product. The values obtained for each indicator are collected and analyzed: as all participant are working on the same technical product with the same dataset, the circularity scores calculated are compared to discuss the reliability and the uncertainty related to these indicators. These new empirical insights are put in parallel with the existing critical analyses of C-indicators from literature. As a result, future research directions on circularity indicators are advanced and discussed, including: the integration of uncertainty considerations into the assessment methodology of circularity indicators; the uptake by industry of such indicators during product design and development; the link between circularity and sustainability scores.

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Supplemental Material for Spiritually Integrated Psychotherapies in Real-World Clinical Practice: Synthesizing the Literature to Identify Best Practices and Future Research Directions

Psychotherapy ◽

10.1037/pst0000407.supp ◽

2021 ◽

Keyword(s):

Clinical Practice ◽

Best Practices ◽

Real World ◽

Future Research ◽

Research Directions ◽

Future Research Directions

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Graph Neural Networks: Taxonomy, Advances, and Trends

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3495161 ◽

2022 ◽

Vol 13 (1) ◽

pp. 1-54

Author(s):

Yu Zhou ◽

Haixia Zheng ◽

Xin Huang ◽

Shufeng Hao ◽

Dengao Li ◽

...

Keyword(s):

Neural Networks ◽

Real World ◽

Research Community ◽

Future Research ◽

Research Directions ◽

Comprehensive Review ◽

Future Research Directions ◽

Graph Neural Networks ◽

Low Dimensional

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers cannot see a panorama of the graph neural networks. This survey aims to overcome this limitation and provide a systematic and comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 327 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the challenges faced. It is expected that more and more scholars can understand and exploit the graph neural networks and use them in their research community.

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