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Nanomaterial grouping: Existing approaches and future recommendations
The physico-chemical properties of manufactured nanomaterials (NMs) can be fine-tuned to obtain different functionalities addressing the needs of specific industrial applications. The physico-chemical properties of NMs also drive their biological interactions. Accordingly, each NM requires an adequate physico-chemical characterization and potentially an extensive and time-consuming (eco)toxicological assessment, depending on regulatory requirements. Grouping and read-across approaches, which have already been established for chemicals in general, are based on similarity between substances and can be used to fill data gaps without performing additional testing. Available data on “source” chemicals are thus used to predict the fate, toxicokinetics and/or (eco)toxicity of structurally similar “target” chemical(s). For NMs similar approaches are only beginning to emerge and several challenges remain, including the identification of the most relevant physico-chemical properties for supporting the claim of similarity. In general, NMs require additional parameters for a proper physico-chemical description. Furthermore, some parameters change during a NM's life cycle, suggesting that also the toxicological profile may change.
This paper compares existing concepts for NM grouping, considering their underlying basic principles and criteria as well as their applicability for regulatory and other purposes. Perspectives and recommendations based on experiences obtained during the EU Horizon 2020 project NanoReg2 are presented. These include, for instance, the importance of harmonized data storage systems, the application of harmonized scoring systems for comparing biological responses, and the use of high-throughput and other screening approaches. We also include references to other ongoing EU projects addressing some of these challenges.
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Recommendation technologies are widespread in streaming services, e-commerce, social media, news, and content management. Besides recommendation generation, its presentation is also important. Most research and development focus on the technical aspects of recommendation generation; therefore, a gap exists between recommendation generation and its effective presentation and user interaction. This study focuses on how personalized recommendations can be presented and interacted with in a music recommendation system using interactive visual interfaces. Interactive interface modeling with User-Centered Design (UCD) in a recommendation system is essential for creating a user-friendly, engaging, and personalized experience. By involving users in the recommendation process and considering their feedback, the system can deliver more relevant content, foster user trust, and improve overall user satisfaction and engagement. In this study, the visual interface design and development of a personalized music recommendation prototype (MusicReco) are presented using an iterative UCD approach, involving twenty end-users, one researcher, three academic professionals, and four experts. As the study is more inclined toward the recommendation presentation and visual modeling, we used a standard content-based filtering algorithm on the publicly available Spotify dataset for music recommendation generation. End-users helped to mature the MusicReco prototype to a basic working version through continuous feedback and design inputs on their needs, context, preferences, personalization, and effective visualization. Moreover, MusicReco captures the idea of mood-based tailored recommendations to encourage end-users. Overall, this study demonstrates how UCD can enhance the presentation and interaction of mood-based music recommendations, effectively engaging users with advancements in recommendation algorithms as a future focus.
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