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This paper examines the ethical and legal challenges encountered during the GATEKEEPER Project and how these challenges informed the development of a comprehensive framework for future Large-Scale Pilot (LSP) projects. GATEKEEPER is a LSP Project with 48 partners conducting 30 implementation studies across Europe with 50,000 target participants grouped into 9 Reference Use Cases. The project underscored the complexity of obtaining ethical approval across various jurisdictions with divergent regulations and procedures. Through a detailed analysis of the issues faced and the strategies employed to navigate these challenges, this study proposes an ethical and legal framework. This framework, derived from a comparative analysis of ethical application forms and regulations, aims to streamline the ethical approval process for future LSP research projects. By addressing the hurdles encountered in GATEKEEPER, the proposed framework offers a roadmap for more efficient and effective project management, ensuring smoother implementation of similar projects in the future. [ABSTRACT FROM AUTHOR]

Regulatory frameworks for artificial intelligence (AI) are needed to mitigate risks while ensuring the ethical, secure, and effective implementation of AI technology in healthcare and population health. In this article, we present a synthesis of 141 binding policies applicable to AI in healthcare and population health in the EU and 10 European countries. The EU AI Act sets the overall regulatory framework for AI, while other legislations set social, health, and human rights standards, address the safety of technologies and the implementation of innovation, and ensure the protection and safe use of data. Regulation specifically pertaining to AI is still nascent and scarce, though a combination of data, technology, innovation, and health and human rights policy has already formed a baseline regulatory framework for AI in health. Future work should explore specific regulatory challenges, especially with respect to AI medical devices, data protection, and data enablement. [ABSTRACT FROM AUTHOR]

This paper explores written calculation methods for division used by pupils in England (n = 276) and the Netherlands (n = 259) at two points in the same school year. Informal strategies are analysed and progression identified towards more structured procedures that result from different teaching approaches. Comparison of the methods used by year 5 (Group 6) pupils in the two countries shows greater success in the Dutch approach, which is based on careful progression from informal strategies to more structured and efficient procedures. This success is particularly notable for the girls in the sample. For the English pupils, whose written solutions largely involved the traditional algorithm, the discontinuity between the formal computation procedure and informal solution strategies presents difficulties. [ABSTRACT FROM AUTHOR]

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Background: The new REACH legislation requires assessment of a large number of chemicals in the European market for several endpoints. Developmental toxicity is one of the most difficult endpoints to assess, on account of the complexity, length and costs of experiments. Following the encouragement of QSAR (in silico) methods provided in the REACH itself, the CAESAR project has developed several models. Results: Two QSAR models for developmental toxicity have been developed, using different statistical/ mathematical methods. Both models performed well. The first makes a classification based on a random forest algorithm, while the second is based on an adaptive fuzzy partition algorithm. The first model has been implemented and inserted into the CAESAR on-line application, which is java-based software that allows everyone to freely use the models. Conclusions: The CAESAR QSAR models have been developed with the aim to minimize false negatives in order to make them more usable for REACH. The CAESAR on-line application ensures that both industry and regulators can easily access and use the developmental toxicity model (as well as the models for the other four endpoints). [ABSTRACT FROM AUTHOR]

A new multi-trajectory highly vectorizable Gear chemical-transport model is discussed and tested against measurements from 25 sites in Europe for a two-week summer period. The simulations are compared with measurement data and with results of a Quasi-Steady-State Approximation (QSSA) based Lagrangian model using the EMEP mechanism.The model is developed from the Lagrangian EMEP model. The Regional Atmospheric Chemistry Mechanism (RACM) is used as the model’s chemical mechanism. To solve the stiff chemical rate equations, a sparse-matrix highly vectorizable Gear algorithm is used. The meteorological data used to run the model are obtained from the HIgh Resolution Limited Area Model (HIRLAM).The vectorized Gear based model improves the accuracy of the numerical solution for the chemistry compared with the QSSA based model. The differences between the two models are explained by alternative approaches of the chemical modules employed in the model: a Gear algorithm versus a QSSA solver, photolysis rate parameters in RACM are calculated from a radiation transfer code versus parameterized photolysis rate parameters used in the Lagrangian EMEP model, the RACM versus the EMEP atmospheric chemical mechanisms and two methods of aggregating the emissions of Volatile Organic Compounds (VOC). Photolysis rate parameters calculated from radiation codes (which are more accurate than simple parameterizations) and the Gear algorithm (which is a benchmark solver compared with the QSSA solver) are recommended for atmospheric chemistry modeling because of the high sensitivity of ozone concentrations to the chemical reaction scheme and to the photolysis rates (Stockwell and Goliff, 2004). [ABSTRACT FROM AUTHOR]