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Results: 78,744
Number of items: 78,744
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Klaver, K. M., Duijts, S. F. A., Geusgens, C. A. V., Kieffer, J. M., Agelink van Rentergem, J., Hendriks, M. P., Nuver, J., Marsman, H. A., Poppema, B. J., Oostergo, T., Doeksen, A., Aarts, M. J. B., Ponds, R. W. H. M., van der Beek, A. J., & Schagen, S. B. (2024). Internet-based cognitive rehabilitation for working cancer survivors: results of a multicenter randomized controlled trial. JNCI Cancer Spectrum, 8(1), Article pkad110. https://doi.org/10.1093/jncics/pkad110 -
Schiffer, B. F., van Vreumingen, D., Tura, J., & Polla, S. (2024). Virtual mitigation of coherent non-adiabatic transitions by echo verification. Quantum, 8, Article 1346. https://doi.org/10.22331/q-2024-05-14-1346, https://doi.org/10.48550/arXiv.2307.10358 -
Papaevangelou, C. (2024). What can we learn from the agreements between platforms and news publishers in France? In M. L. Young, & A. Hermida (Eds.), Novel Directions in Media Innovation and Funding (pp. 83-86). The Global Journalism Innovation Lab, University of British Columbia. https://doi.org/10.14288/1.0440952 -
Carlson, J., Incerti, T., & Aronow, P. M. (2024). Dyadic clustering in international relations. Political Analysis, 32(2), 186-198. https://doi.org/10.1017/pan.2023.26 -
Mahes, R., Mandjes, M., & Boon, M. (2024). Adaptive appointment scheduling with periodic updates. Computers and Operations Research, 161, Article 106437. https://doi.org/10.1016/j.cor.2023.106437 -
Nielsen, K. S., Cologna, V., Bauer, J. M., Berger, S., Brick, C., Dietz, T., Hahnel, U. J. J., Henn, L., Lange, F., Stern, P. C., & Wolske, K. S. (2024). Realizing the full potential of behavioural science for climate change mitigation. Nature Climate Change, 14(4), 322-330. https://doi.org/10.1038/s41558-024-01951-1 -
Lok, M. (2024). Illiberal Ideas: An Anatomy of Intellectual Historians and Illiberalism. Journal of Illiberalism Studies, 4(2), 47-57. https://doi.org/10.53483/XCPV3575 -
Tello, A., Truong, H., Lazovik, A., & Degeler, V. (2024). Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks. Engineering Proceedings, 69, Article 50. https://doi.org/10.3390/engproc2024069050
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