Search results
Results: 80,394
Number of items: 80,394
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Rahbari, A., Hens, R., Jamali, S. H., Ramdin, M., Dubbeldam, D., & Vlugt, T. J. H. (2019). Effect of truncating electrostatic interactions on predicting thermodynamic properties of water-methanol systems. Molecular Simulation, 45(4-5), 336-350. https://doi.org/10.1080/08927022.2018.1547824 -
van Rooij, R., & Schulz, K. (2019). Generic sentences: Representativeness or Causality? In M. T. Espinal, E. Castroviejo, M. Leonetti, L. McNally, & C. Real-Puigdollers (Eds.), Proceedings of Sinn und Bedeutung 23 (Vol. 2, pp. 409-425). Universitat Autònoma de Barcelona. https://doi.org/10.18148/sub/2019.v23i2.621
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Barok, D., Noordegraaf, J., & de Vries, A. P. (2019). From Collection Management to Content Management in Art Documentation: The Conservator as an Editor. Studies in Conservation, 64(8), 472-489. https://doi.org/10.1080/00393630.2019.1603921
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Rodríguez Sánchez, F., Arteagoitia, I., Rodríguez Andrés, C., & Bruers, J. (2019). Antibiotic prophylaxis prescribing habits in oral implant surgery in the Netherlands: a cross-sectional survey. BMC Oral Health, 19(1), Article 281. https://doi.org/10.1186/s12903-019-0981-4
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Hin, V., & de Roos, A. M. (2019). Cannibalism prevents evolutionary suicide of ontogenetic omnivores in life-history intraguild predation systems. Ecology and Evolution, 9(7), 3807-3822. https://doi.org/10.1002/ece3.5004
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Bergstra, J. A., & Middelburg, C. A. (2019). Process Algebra with Strategic Interleaving. Theory of Computing Systems, 63(3), 488–505 . https://doi.org/10.1007/s00224-018-9873-2
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Deuze, M. (2019). On Creativity. Journalism, 20(1), 130-134 . https://doi.org/10.1177/1464884918807066
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Aaboud, M., & The ATLAS Collaboration (2019). Measurement of the tt¯Z and tt¯W cross sections in proton-proton collisions at √s =13 TeV with the ATLAS detector. Physical Review D. Particles and Fields, 99(7), Article 072009. https://doi.org/10.1103/PhysRevD.99.072009
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Vermeer, S. A. M., Araujo, T., Bernritter, S. F., & van Noort, G. (2019). Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media. International Journal of Research in Marketing, 36(3), 492-508. https://doi.org/10.1016/j.ijresmar.2019.01.010
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