Evaluating machine learning models and identifying key factors influencing spatial maize yield predictions in data intensive farm management

Autor: Maseko, S., van der Laan, M., Tesfamariam, E.H., Delport, M., Otterman, H.

Publikováno v: In European Journal of Agronomy July 2024 157

Deep-sea deployment of the KM3NeT neutrino telescope detection units by self-unrolling

Autor: The KM3NeT Collaboration, Aiello, S., Albert, A., Garre, S. Alves, Aly, Z., Ameli, F., Anassontzis, E. G., Andre, M., Androulakis, G., Anghinolfi, M., Anguita, M., Anton, G., Ardid, M., Aublin, J., Bagatelas, C., Bakker, R., Barbarino, G., Baret, B., Pree, S. Basegmez du, Bendahman, M., Berbeen, E., Berg, A. M. van den, Bertin, V., Biagi, S., Billault, M., Bissinger, M., Boettcher, M., Boumaaza, J., Bouta, M., Bouwhuis, M., Bozza, C., Brânzaş, H., Bruijn, R., Brunner, J., Buis, E., Buompane, R., Busto, J., Cacopardo, G., Caiffi, B., Caillat, L., Calvo, D., Capone, A., Carretero, V., Castaldi, P., Celli, S., Chabab, M., Chau, N., Chen, A., Cherubini, S., Chiarella, V., Chiarusi, T., Circella, M., Cocimano, R., Coelho, J. A. B., Coleiro, A., Molla, M. Colomer, Colonges, S., Coniglione, R., Corredoira, I., Cosquer, A., Coyle, P., Creusot, A., Cuttone, G., D'Amato, C., D'Onofrio, A., Dallier, R., De Palma, M., Di Palma, I., Díaz, A. F., Diego-Tortosa, D., Distefano, C., Domi, A., Donà, R., Donzau, C., Dornic, D., Dörr, M., Drouhin, D., Eberl, T., Eddyamoui, A., van Eeden, T., van Eijk, D., Bojaddaini, I. El, Elsaesser, D., Enzenhöfer, A., Espinosa, V., Fermani, P., Ferrara, G., Filipović, M. D., Filippini, F., Fusco, L. A., Gabella, O., Gal, T., Soto, A. Garcia, Garufi, F., Gatelet, Y., Geißelbrecht, N., Gialanella, L., Giorgio, E., Gostiaux, L., Gozzini, S. R., Gracia, R., Graf, K., Grasso, D., Grella, G., Grmek, A., Guderian, D., Guidii, C., Hallmann, S., Hamdaoui, H., van Haren, H., van Heerwaarden, J., Heijboer, A., Hekaloa, A., Henry, S., Hernández-Rey, J. J., Hillebrand, T., Hofestädt, J., Huang, F., Ibnsalih, W. Idrissi, Ilioni, A., Illuminati, G., James, C. W., de Jong, M., de Jong, P., Jung, B. J., Kadler, M., Kalaczyński, P., Kalekin, O., Katz, U. F., Chowdhury, N. R. Khan, Kistauri, G., van der Knaap, F., Koffeman, E. N., Kooijman, P., Kouchner, A., Kreter, M., Kulikovskiy, V., Laan, M., Lahmann, R., Lamare, P., Larosa, G., Laurence, J., Breton, R. Le, Leonardi, O., Leone, F., Leonora, E., Levi, G., Lincetto, M., Clark, M. Lindsey, Lipreau, T., Longhitano, F., LopezCotoa, D., Maderer, L., Mańczak, J., Mannheim, K., Margiotta, A., Marinelli, A., Markou, C., Martin, L., Martínez-Mora, J. A., Martini, A., Marzaioli, F., Mastroianni, S., Mazzou, S., Melis, K. W., Miele, G., Migliozzi, P., Migneco, E., Mijakowski, P., Miranda, L. S., Mollo, C. M., Mongelli, M., Morganti, M., Moser, M., Moussa, A., Muller, R., Pérez, D. Muñoz, Musumeci, M., Nauta, L., Navas, S., Nicolau, C. A., Fearraigh, B. Ó, O'Sullivan, M., Organokov, M., Orlando, A., González, J. Palacios, Papalashvili, G., Papaleo, R., Pastore, C., Păun, A. M., Păvălaş, G. E., Pellegrino, C., Perrin-Terrin, M., Piattelli, P., Pieterse, C., Pikounis, K., Pisanti, O., Poirè, C., Popa, V., Pradier, T., Pühlhofer, G., Pulvirenti, S., Rabyang, O., Raffaelli, F., Randazzo, N., Razzaque, S., Real, D., Reck, S., Riccobene, G., Richer, M., Rivoire, S., Rovelli, A., Greus, F. Salesa, Samtleben, D. F. E., Losa, A. Sánchez, Sanguineti, M., Santangelo, A., Santonocito, D., Sapienza, P., Schnabel, J., Schumann, J., Seneca, J., Sgura, I., Shanidze, R., Sharma, A., Simeone, F., Sinopoulou, A., Spisso, B., Spurio, M., Stavropoulos, D., Steijger, J., Stellacci, S. M., Taiutii, M., Tayalati, Y., Tenllado, E., Tézier, D., Thakore, T., Tingay, S., Tzamariudaki, E., Tzanetatos, D., Van Elewyck, V., Vasileiadis, G., Versari, F., Viola, S., Vivolo, D., de Wasseige, G., Wilms, J., Wojaczyński, R., de Wolf, E., Zavatarelli, S., Zegarelli, A., Zito, D., Zornoza, J. D., Zúñiga, J., Zywucka, N.

KM3NeT is a research infrastructure being installed in the deep Mediterranean Sea. It will house a neutrino telescope comprising hundreds of networked moorings - detection units or strings equipped with optical instrumentation to detect the Cherenkov

Externí odkaz: http://arxiv.org/abs/2007.16090

Expert-Augmented Machine Learning

Autor: Gennatas, E. D., Friedman, J. H., Ungar, L. H., Pirracchio, R., Eaton, E., Reichman, L., Interian, Y., Simone, C. B., Auerbach, A., Delgado, E., Van der Laan, M. J., Solberg, T. D., Valdes, G.

Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is common

Externí odkaz: http://arxiv.org/abs/1903.09731

Constraint on a varying proton-to-electron mass ratio from H2 and HD absorption at z = 2.34

Autor: Daprà, M., van der Laan, M., Murphy, M. T., Ubachs, W.

Molecular hydrogen absorption in the damped Lyman-alpha system at z = 2.34 towards quasar Q1232+082 is analyzed in order to derive a constraint on a possible temporal variation of the proton-to-electron mass ratio, mu, over cosmological timescales. S

Externí odkaz: http://arxiv.org/abs/1611.05191

Verification and Validation of Wind Farm Flow Models.

Autor: Quick, Julian, Mouradi, Rem-Sophia, Devesse, Koen, Mathieu, Antoine, Paul Van Der Laan, M., Murcia Leon, Juan Pablo, Schulte, Jonas

Publikováno v: Journal of Physics: Conference Series; 2024, Vol. 2767 Issue 1, p1-10, 10p