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Ye, D., Krzhizhanovskaya, V., & Hoekstra, A. G. (2024). Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots. Journal of computational Physics, 497, Article 112639. https://doi.org/10.1016/j.jcp.2023.112639 -
Votta, F., Kruschinski, S., Hove, M., Helberger, N., Dobber, T., & de Vreese, C. (2024). Who Does(n't) Target You? Mapping the Worldwide Usage of Online Political Microtargeting. Journal of Quantitative Description: Digital Media, 4. https://doi.org/10.51685/jqd.2024.010 -
van Berlo, Z. M. C., Campbell, C., & Voorveld, H. A. M. (2024). The MADE framework: Best practices for creating effective experimental stimuli using generative AI. Journal of Advertising, 53(5), 732–753. https://doi.org/10.1080/00913367.2024.2397777 -
Dorozsmai, A., & Toonen, S. (2024). Importance of stable mass transfer and stellar winds for the formation of gravitational wave sources. Monthly Notices of the Royal Astronomical Society, 530(4), 3706-3739. https://doi.org/10.1093/mnras/stae152 -
Bravo, M., & Schoepf, V. (2024). Growth rates of five coral species across a strong environmental gradient in the Colombian Caribbean. Marine Biology, 171(9), Article 187. https://doi.org/10.1007/s00227-024-04511-5 -
Brown, P., Scrivener, A., & Calnan, M. (2024). The co-construction of emotion and management of hope within psychosis services. Frontiers in Sociology, 8, Article 1270539. https://doi.org/10.3389/fsoc.2023.1270539 -
Thanh Si, N., Vu Nhat, P., Vo Anh Duy, N., Thi Bao Trang, N., Nhan Tran, T., Chi Ben, N., Anh Nguyen, T., Triet Dang, M., Schall, P., & An Dinh, V. (2024). Polaronic defect enhances optoelectronic and transport properties of blue phosphorene quantum dots using first-principles methods. Computational materials science, 241, Article 113020. https://doi.org/10.1016/j.commatsci.2024.113020 -
Eltink, E., & Bröer, C. (2024). Healthism online: ‘What I eat in a day’ vlogs before and during COVID-19 restrictions. Heliyon, 10(1), Article e23623. https://doi.org/10.1016/j.heliyon.2023.e23623 -
Zhang, Y., Doughty, H., & Snoek, C. G. M. (2024). Low-Resource Vision Challenges for Foundation Models. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2024 : Seattle, Washington, USA, 16-22 June 2024 : proceedings (pp. 21956-21966). IEEE Computer Society. https://doi.org/10.48550/arXiv.2401.04716, https://doi.org/10.1109/CVPR52733.2024.02073
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