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of 749
pro vyhledávání: '"Doan, N."'
AC losses in conductor-on-rounded-core (CORC) cables of YBCO high-temperature superconducting (HTS) tapes are a significant challenge in HTS power applications. This study employs two finite element analysis (FEA) models to investigate the contributi
Externí odkaz:
http://arxiv.org/abs/2306.04559
Publikováno v:
Tạp chí Khoa học và Công nghệ, Pp 36-41 (2024)
Xây dựng một hệ thống sản xuất trải qua nhiều quá trình như: lập kế hoạch, thiết kế, mô phỏng, thử nghiệm và triển khai thực tế. Trong đó, quá trình quan trọng nhất trước khi triển khai xây d
Externí odkaz:
https://doaj.org/article/6b355febd0124d379906047da1557190
Autor:
Schönemann, Rico, Rosa, Priscila F. S., Thomas, Sean M., Lai, You, Nguyen, Doan N., Singleton, John, Brosha, Eric L., McDonald, Ross D., Zapf, Vivien, Maiorov, Boris, Jaime, Marcelo
There has been a recent surge of interest in UTe$_2$ due to its unconventional magnetic field (H) reinforced spin-triplet superconducting phases persisting at fields far above the simple Pauli limit for H $\parallel$ [010]. Magnetic fields in excess
Externí odkaz:
http://arxiv.org/abs/2206.06508
Autor:
Sun, Dan, Naud, Martin F., Nguyen, Doan N, Betts, Jonathan B, Singleton, John, Balakirev, Fedor F
Publikováno v:
Review of Scientific Instruments 92, 023903 (2021)
Extreme pressures and high magnetic fields can affect materials in profound and fascinating ways. However, large pressures and fields are often mutually incompatible; the rapidly changing fields provided by pulsed magnets induce eddy currents in the
Externí odkaz:
http://arxiv.org/abs/2008.07619
Publikováno v:
Journal of Applied Physics 127, 215103 (2020)
La(Fe,Si)$_{13}$-based compounds are considered to be very promising magnetocaloric materials for magnetic refrigeration applications. Many studies have focused on this material family but only in bulk form. In this paper, we report the fabrication o
Externí odkaz:
http://arxiv.org/abs/2006.01656
Akademický článek
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Publikováno v:
Journal of Applied Physics; 10/14/2023, Vol. 134 Issue 14, p1-15, 15p
Akademický článek
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Autor:
Petrov, Dmitry, Kuznetsov, Boris A. Gutman Egor, van Erp, Theo G. M., Turner, Jessica A., Schmaal, Lianne, Veltman, Dick, Wang, Lei, Alpert, Kathryn, Isaev, Dmitry, Zavaliangos-Petropulu, Artemis, Ching, Christopher R. K., Calhoun, Vince, Glahn, David, Satterthwaite, Theodore D., Andreassen, Ole Andreas, Borgwardt, Stefan, Howells, Fleur, Groenewold, Nynke, Voineskos, Aristotle, Radua, Joaquim, Potkin, Steven G., Crespo-Facorro, Benedicto, Tordesillas-Gutierrez, Diana, Shen, Li, Lebedeva, Irina, Spalletta, Gianfranco, Donohoe, Gary, Kochunov, Peter, Rosa, Pedro G. P., James, Anthony, Dannlowski, Udo, Baune, Bernhard T., Aleman, Andre, Gotlib, Ian H., Walter, Henrik, Walter, Martin, Soares, Jair C., Ehrlich, Stefan, Gur, Ruben C., Doan, N. Trung, Agartz, Ingrid, Westlye, Lars T., Harrisberger, Fabienne, Riecher-Rossler, Anita, Uhlmann, Anne, Stein, Dan J., Dickie, Erin W., Pomarol-Clotet, Edith, Fuentes-Claramonte, Paola, Canales-Rodriguez, Erick Jorge, Salvador, Raymond, Huang, Alexander J., Roiz-Santianez, Roberto, Cong, Shan, Tomyshev, Alexander, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Ciullo, Valentina, Hong, Elliot, Busatto, Geraldo, Zanetti, Marcus V., Serpa, Mauricio H., Cervenka, Simon, Kelly, Sinead, Grotegerd, Dominik, Sacchet, Matthew D., Veer, Ilya M., Li, Meng, Wu, Mon-Ju, Irungu, Benson, Walton, Esther, Thompson, Paul M.
We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional neural net architectures (VGGNet, ResNet and Inception) over 2D maps of
Externí odkaz:
http://arxiv.org/abs/1808.10315
Autor:
Petrov, Dmitry, Gutman, Boris A., Shih-Hua, Yu, van Erp, Theo G. M., Turner, Jessica A., Schmaal, Lianne, Veltman, Dick, Wang, Lei, Alpert, Kathryn, Isaev, Dmitry, Zavaliangos-Petropulu, Artemis, Ching, Christopher R. K., Calhoun, Vince, Glahn, David, Satterthwaite, Theodore D., Andreasen, Ole Andreas, Borgwardt, Stefan, Howells, Fleur, Groenewold, Nynke, Voineskos, Aristotle, Radua, Joaquim, Potkin, Steven G., Crespo-Facorro, Benedicto, Tordesillas-Gutierrez, Diana, Shen, Li, Lebedeva, Irina, Spalletta, Gianfranco, Donohoe, Gary, Kochunov, Peter, Rosa, Pedro G. P., James, Anthony, Dannlowski, Udo, Baune, Bernhard T., Aleman, Andre, Gotlib, Ian H., Walter, Henrik, Walter, Martin, Soares, Jair C., Ehrlich, Stefan, Gur, Ruben C., Doan, N. Trung, Agartz, Ingrid, Westlye, Lars T., Harrisberger, Fabienne, Riecher-Rossler, Anita, Uhlmann, Anne, Stein, Dan J., Dickie, Erin W., Pomarol-Clotet, Edith, Fuentes-Claramonte, Paola, Canales-Rodriguez, Erick Jorge, Salvador, Raymond, Huang, Alexander J., Roiz-Santianez, Roberto, Cong, Shan, Tomyshev, Alexander, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Ciullo, Valentina, Hong, Elliot, Busatto, Geraldo, Zanetti, Marcus V., Serpa, Mauricio H., Cervenka, Simon, Kelly, Sinead, Grotegerd, Dominik, Sacchet, Matthew D., Veer, Ilya M., Li, Meng, Wu, Mon-Ju, Irungu, Benson, Walton, Esther, Thompson, Paul M.
As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In th
Externí odkaz:
http://arxiv.org/abs/1707.06353