Zobrazeno 1 - 10
of 36 072
pro vyhledávání: '"Schnabel"'
Autor:
Schnabel, Pascal
Untypische Zahnradgetriebebauformen ermöglichen es, die technische Grenzen von Standardgetriebeformen zu überwinden. Beispielsweise ermöglicht das perizyklische Getriebe oder das triaxiale Getriebe „Hypogear“, eine sehr hohe Übersetzung auf k
Externí odkaz:
https://monarch.qucosa.de/id/qucosa%3A91741
https://monarch.qucosa.de/api/qucosa%3A91741/attachment/ATT-0/
https://monarch.qucosa.de/api/qucosa%3A91741/attachment/ATT-0/
Autor:
Schnabel, Kai Philipp, Lörwald, Andrea Carolin, Beltraminelli, Helmut, Germano, Miria, Brem, Beate Gabriele, Wüst, Sandra, Bauer, Daniel
Publikováno v:
GMS Journal for Medical Education, Vol 41, Iss 2, p Doc14 (2024)
Modern medical moulages are becoming increasingly important in simulation-based health professions education. Their lifelikeness is important so that simulation engagement is not disrupted while their standardization is crucial in high-stakes exams.
Externí odkaz:
https://doaj.org/article/98f138fc61034353b6f2e4cc0c8ad6c7
Autor:
Doan LV, Li A, Brake L, Ok D, Jee HJ, Park H, Cuevas R, Calvino S, Guth A, Schnabel F, Hiotis K, Axelrod D, Wang J
Publikováno v:
Journal of Pain Research, Vol Volume 16, Pp 881-892 (2023)
Lisa V Doan,1 Anna Li,1 Lee Brake,1 Deborah Ok,1 Hyun Jung Jee,1 Hyung Park,2 Randy Cuevas,1 Steven Calvino,1 Amber Guth,3 Freya Schnabel,3 Karen Hiotis,3 Deborah Axelrod,3 Jing Wang1,4 1Department of Anesthesiology, Perioperative Care and Pain Medic
Externí odkaz:
https://doaj.org/article/a8f25cb579b048ccb00010ee6eb1e4a7
Publikováno v:
EPJ Web of Conferences, Vol 294, p 04001 (2024)
One of the most important sources of systematic uncertainties in the evaluation of measured cross sections is the absolute normalization of every dataset, which were often performed by measuring simultaneously the reference cross-section of the stand
Externí odkaz:
https://doaj.org/article/9fdf58d5f8724e56b83d4851f6ae9f88
Publikováno v:
EPJ Web of Conferences, Vol 292, p 12003 (2024)
Efficient data mining from the Experimental Nuclear Reaction Database (EXFOR) has a potential for utilization of modern computational analysis techniques to find trends, shortcomings and hidden patterns in the database, which in turn helps improve ou
Externí odkaz:
https://doaj.org/article/a660c61d064044f686e6e674131474b4
Deep learning models in medical imaging often encounter challenges when adapting to new clinical settings unseen during training. Test-time adaptation offers a promising approach to optimize models for these unseen domains, yet its application in ano
Externí odkaz:
http://arxiv.org/abs/2410.03306
Autor:
Aiello, S., Albert, A., Alhebsi, A. R., Alshamsi, M., Garre, S. Alves, Ambrosone, A., Ameli, F., Andre, M., Aphecetche, L., Ardid, M., Ardid, S., Atmani, H., Aublin, J., Badaracco, F., Bailly-Salins, L., Bardacova, Z., Baret, B., Bariego-Quintana, A., Becherini, Y., Bendahman, M., Benfenati, F., Benhassi, M., Bennani, M., Benoit, D. M., Berbee, E., Bertin, V., Biagi, S., Boettcher, M., Bonanno, D., Bouasla, A. B., Boumaaza, J., Bouta, M., Bouwhuis, M., Bozza, C., Bozza, R. M., Branzas, H., Bretaudeau, F., Breuhaus, M., Bruijn, R., Brunner, J., Bruno, R., Buis, E., Buompane, R., Busto, J., Caiffi, B., Calvo, D., Capone, A., Carenini, F., Carretero, V., Cartraud, T., Castaldi, P., Cecchini, V., Celli, S., Cerisy, L., Chabab, M., Chen, A., Cherubini, S., Chiarusi, T., Circella, M., Cocimano, R., Coelho, J. A. B., Coleiro, A., Condorelli, A., Coniglione, R., Coyle, P., Creusot, A., Cuttone, G., Dallier, R., De Benedittis, A., De Martino, B., De Wasseige, G., Decoene, V., Del Rosso, I., Di Mauro, L. S., Di Palma, I., Diaz, A. F., Diego-Tortosa, D., Distefano, C., Domi, A., Donzaud, C., Dornic, D., Drakopoulou, E., Drouhin, D., Ducoin, J. -G., Dvornicky, R., Eberl, T., Eckerova, E., Eddymaoui, A., van Eeden, T., Eff, M., van Eijk, D., Bojaddaini, I. El, Hedri, S. El, Ellajosyula, V., Enzenhoefer, A., Ferrara, G., Filipovic, M. D., Filippini, F., Franciotti, D., Fusco, L. A., Gagliardini, S., Gal, T., Mendez, J. Garcia, Soto, A. Garcia, Oliver, C. Gatius, Geißelbrecht, N., Genton, E., Ghaddari, H., Gialanella, L., Gibson, B. K., Giorgio, E., Goos, I., Goswami, P., Gozzini, S. R., Gracia, R., Guidi, C., Guillon, B., Gutierrez, M., Haack, C., van Haren, H., Heijboer, A., Hennig, L., Hernandez-Rey, J. J., Ibnsalih, W. Idrissi, Illuminati, G., Joly, D., de Jong, M., de Jong, P., Jung, B. J., Kistauri, G., Kopper, C., Kouchner, A., Kovalev, Y. Y., Kueviakoe, V., Kulikovskiy, V., Kvatadze, R., Labalme, M., Lahmann, R., Lamoureux, M., Larosa, G., Lastoria, C., Lazo, A., Stum, S. Le, Lehaut, G., Lemaitre, V., Leonora, E., Lessing, N., Levi, G., Clark, M. Lindsey, Longhitano, F., Magnani, F., Majumdar, J., Malerba, L., Mamedov, F., Manczak, J., Manfreda, A., Marconi, M., Margiotta, A., Marinelli, A., Markou, C., Martin, L., Mastrodicasa, M., Mastroianni, S., Mauro, J., Miele, G., Migliozzi, P., Migneco, E., Mitsou, M. L., Mollo, C. M., Morales-Gallegos, L., Moussa, A., Mateo, I. Mozun, Muller, R., Musone, M. R., Musumeci, M., Navas, S., Nayerhoda, A., Nicolau, C. A., Nkosi, B., Fearraigh, B. O., Oliviero, V., Orlando, A., Oukacha, E., Gonzalez, D. Paesaniy J. Palacios, Papalashvili, G., Parisi, V., Gomez, E. J. Pastor, Pastore, C., Paun, A. M., Pavala, G. E., Martinez, S. Pena, Perrin-Terrin, M., Pestel, V., Pestes, R., Piattelli, P., Plavin, A., Poire, C., Popa, V., Pradier, T., Prado, J., Pulvirenti, S., Quiroz-Rangel, C. A., Randazzo, N., Razzaque, S., Rea, I. C., Real, D., Robinson, G. Riccobene. J., Romanov, A., Ros, E., Saina, A., Greus, F. Salesa, Samtleben, D. F. E., Losa, A. Sanchez, Sanfilippo, S., Sanguineti, M., Santonocito, D., Sapienza, P., Schnabel, J., Schumann, J., Schutte, H. M., Seneca, J., Sgura, I., Shanidze, R., Sharma, A., Shitov, Y., Simkovic, F., Simonelli, A., Sinopoulou, A., Spisso, B., Spurio, M., Stavropoulos, D., Stekl, I., Stellacci, S. M., Taiuti, M., Tayalati, Y., Thiersen, H., Thoudam, S., Tosta, I., Melo, e, Trocme, B., Tsourapis, V., Tudorache, A., Tzamariudaki, E., Ukleja, A., Vacheret, A., Valsecchi, V., Van Elewyck, V., Vannoye, G., Vasileiadis, G., de Sola, F. Vazquez, Veutro, A., Viola, S., Vivolo, D., van Vliet, A., de Wolf, E., Lhenry-Yvon, I., Zavatarelli, S., Zegarelli, A., Zito, D., Zornoza, J. D., Zuniga, J., Zywucka, N.
Neutrinos described as an open quantum system may interact with the environment which introduces stochastic perturbations to their quantum phase. This mechanism leads to a loss of coherence along the propagation of the neutrino $-$ a phenomenon commo
Externí odkaz:
http://arxiv.org/abs/2410.01388
Autor:
Osuala, Richard, Joshi, Smriti, Tsirikoglou, Apostolia, Garrucho, Lidia, Pinaya, Walter H. L., Lang, Daniel M., Schnabel, Julia A., Diaz, Oliver, Lekadir, Karim
This paper presents a method for virtual contrast enhancement in breast MRI, offering a promising non-invasive alternative to traditional contrast agent-based DCE-MRI acquisition. Using a conditional generative adversarial network, we predict DCE-MRI
Externí odkaz:
http://arxiv.org/abs/2409.18872
Autor:
Koch, Valentin, Bauer, Sabine, Luppberger, Valerio, Joner, Michael, Schunkert, Heribert, Schnabel, Julia A., von Scheidt, Moritz, Marr, Carsten
Background: The integration of multi-stain histopathology images through deep learning poses a significant challenge in digital histopathology. Current multi-modal approaches struggle with data heterogeneity and missing data. This study aims to overc
Externí odkaz:
http://arxiv.org/abs/2409.17775
Autor:
Rasheed, Hassan, Dorent, Reuben, Fehrentz, Maximilian, Kapur, Tina, Wells III, William M., Golby, Alexandra, Frisken, Sarah, Schnabel, Julia A., Haouchine, Nazim
We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (US) images. We introduce a matching-by-synthesis strategy, where intra
Externí odkaz:
http://arxiv.org/abs/2409.08169