Threshold bounce -- occupancy-dependent modulation of the discriminating threshold in silicon detectors

Autor: Basso, M. J., Buchanan, E., Gallop, B. J., John, J. J., Kaplon, J., Keener, P. T., Phillips, P. W., Poley, L., Sawyer, C. A., Sperlich, D., Warren, M.

Publikováno v: JINST 19 P06041 (2024)

The front-end electronics of silicon detectors are typically designed to ensure optimal noise performance for the expected input charge. A combination of preamplifiers and shaper circuits result in a nontrivial response of the front-end to injected c

Externí odkaz: http://arxiv.org/abs/2406.10747

Technical Design Report for the LUXE Experiment

Autor: Abramowicz, H., Soto, M. Almanza, Altarelli, M., Aßmann, R., Athanassiadis, A., Avoni, G., Behnke, T., Benettoni, M., Benhammou, Y., Bhatt, J., Blackburn, T., Blanch, C., Bonaldo, S., Boogert, S., Borysov, O., Borysova, M., Boudry, V., Breton, D., Brinkmann, R., Bruschi, M., Burkart, F., Büßer, K., Cavanagh, N., Corso, F. Dal, Decking, W., Deniaud, M., Diner, O., Dosselli, U., Elad, M., Epshteyn, L., Esperante, D., Ferber, T., Firlej, M., Fiutowski, T., Fleck, K., Fuster-Martinez, N., Gadow, K., Gaede, F., Gallas, A., Cabrera, H. Garcia, Gerstmayr, E., Ghenescu, V., Giorato, M., Golubeva, N., Grojean, C., Grutta, P., Grzelak, G., Hallford, J., Hartman, L., Heinemann, B., Heinzl, T., Helary, L., Hendriks, L., Hoffmann, M., Horn, D., Huang, S., Huang, X., Idzik, M., Irles, A., Jacobs, R., King, B., Klute, M., Kropf, A., Kroupp, E., Lahno, H., Manghi, F. Lasagni, Lawhorn, J., Levanon, A., Levi, A., Levinson, L., Levy, A., Levy, I., Liberman, A., Liss, B., List, B., List, J., Lohmann, W., Maalmi, J., Madlener, T., Malka, V., Marsault, T., Mattiazzo, S., Meloni, F., Miron, D., Morandin, M., Moroń, J., Nanni, J., Neagu, A. T., Negodin, E., Paccagnella, A., Pantano, D., Pietruch, D., Pomerantz, I., Pöschl, R., Potlog, P. M., Prasad, R., Quishpe, R., Ranken, E., Ringwald, A., Roich, A., Salgado, F., Santra, A., Sarri, G., Sävert, A., Sbrizzi, A., Schmitt, S., Schulthess, I., Schuwalow, S., Seipt, D., Simi, G., Soreq, Y., Spataro, D., Streeter, M., Swientek, K., Hod, N. Tal, Teter, T., Thiebault, A., Thoden, D., Trevisani, N., Urmanov, R., Vasiukov, S., Walker, S., Warren, M., Wing, M., Yap, Y. C., Zadok, N., Zanetti, M., Żarnecki, A. F., Zbińkowski, P., Zembaczyński, K., Zepf, M., Zerwas, D., Ziegler, W., Zuffa, M.

This Technical Design Report presents a detailed description of all aspects of the LUXE (Laser Und XFEL Experiment), an experiment that will combine the high-quality and high-energy electron beam of the European XFEL with a high-intensity laser, to e

Externí odkaz: http://arxiv.org/abs/2308.00515

Highly efficient modeling and optimization of neural fiber responses to electrical stimulation

Autor: Minhaj A. Hussain, Warren M. Grill, Nicole A. Pelot

Publikováno v: Nature Communications, Vol 15, Iss 1, Pp 1-19 (2024)

Abstract Peripheral neuromodulation has emerged as a powerful modality for controlling physiological functions and treating a variety of medical conditions including chronic pain and organ dysfunction. The underlying complexity of the nonlinear respo

Externí odkaz: https://doaj.org/article/de41de8328204e9b9d62ce36cf29f835

Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease Treatment

Autor: Gao, Qitong, Schimdt, Stephen L., Chowdhury, Afsana, Feng, Guangyu, Peters, Jennifer J., Genty, Katherine, Grill, Warren M., Turner, Dennis A., Pajic, Miroslav

Deep brain stimulation (DBS) has shown great promise toward treating motor symptoms caused by Parkinson's disease (PD), by delivering electrical pulses to the Basal Ganglia (BG) region of the brain. However, DBS devices approved by the U.S. Food and

Externí odkaz: http://arxiv.org/abs/2302.02477

Evaluating Artificial Intelligence's Role in Teaching the Reporting and Interpretation of Computed Tomographic Angiography for Preoperative Planning of the Deep Inferior Epigastric Artery Perforator Flap

Autor: Bryan Lim, Jevan Cevik, Ishith Seth, Foti Sofiadellis, Richard J. Ross, Warren M. Rozen, Roberto Cuomo

Publikováno v: JPRAS Open, Vol 40, Iss , Pp 273-285 (2024)

Background: Artificial intelligence (AI) has the potential to transform preoperative planning for breast reconstruction by enhancing the efficiency, accuracy, and reliability of radiology reporting through automatic interpretation and perforator iden

Externí odkaz: https://doaj.org/article/0ec895e504ae4334ae0ada8d6ef89482