Final Design of the Production SSR1 Cryomodule for PIP-II Project at Fermilab

Autor: Bernardini, J., Roger, V., Passarelli, D., Parise, M., Romanov, G., Helsper, J., Chen, M., Kramp, M., Lewis, F., Squires, B., Nicol, T., Neri, P.

This contribution reports the design of the production Single Spoke Resonator Type 1 Cryomodule (SSR1 CM) for the PIP-II project at Fermilab. The innovative design is based on a structure, the strongback, which supports the coldmass from the bottom,

Externí odkaz: http://arxiv.org/abs/2311.05475

Design, Manufacturing, Assembly, and Lessons Learned of the Pre-production 325 MHz Couplers for the PIP-II Project at Fermilab

Autor: Helsper, J., Wallon, S., Passarelli, D., Longuevergne, D., Kazakov, S., Solyak, N.

Five 325 MHz high-power couplers will be integrated into the pre-production Single Spoke Resonator Type-II (ppSSR2) cryomodule for the PIP-II project at Fermilab. Couplers were procured by both Fermilab and IJCLAB for this effort. The design of the c

Externí odkaz: http://arxiv.org/abs/2307.05771

Design, Manufacturing, Assembly, Testing, And Lessons Learned Of The Prototype 650 Mhz Couplers

Autor: Helsper, J., Chandrasekaran, S., Solyak, N., Kazakov, S., Premo, K., Wu, G., Furuta, F., Ozelis, J., Hanna, B.

Six 650 MHz high-power couplers will be integrated into the prototype High Beta 650 MHz (HB650) cryomodule for the PIP-II project at Fermilab. The design of the coupler is described, including design optimizations from the previous generation. This p

Externí odkaz: http://arxiv.org/abs/2209.01283

DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images.

Autor: Nasiri S; Department of Electrical Engineering and Computer Science, University of Siegen, Hölderlinstr. 3, Siegen, Germany. sara.nasiri@uni-siegen.de., Helsper J; Department of Electrical Engineering and Computer Science, University of Siegen, Hölderlinstr. 3, Siegen, Germany., Jung M; Department of Electrical Engineering and Computer Science, University of Siegen, Hölderlinstr. 3, Siegen, Germany., Fathi M; Department of Electrical Engineering and Computer Science, University of Siegen, Hölderlinstr. 3, Siegen, Germany.

Publikováno v: BMC bioinformatics [BMC Bioinformatics] 2020 Mar 11; Vol. 21 (Suppl 2), pp. 84. Date of Electronic Publication: 2020 Mar 11.

Deep Learning Techniques for the Dermoscopic Differential Diagnosis of Benign/Malignant Melanocytic Skin Lesions: From the Past to the Present.

Autor: Tognetti, Linda¹ linda.tognetti@dbm.unisi.it, Miracapillo, Chiara¹, Leonardelli, Simone¹, Luschi, Alessio² ernesto.iadanza@unisi.it, Iadanza, Ernesto², Cevenini, Gabriele², Rubegni, Pietro¹, Cartocci, Alessandra^1,2

Publikováno v: Bioengineering (Basel). Aug2024, Vol. 11 Issue 8, p758. 25p.