Results 171 to 180 of about 178,776 (288)

Informer-based DDoS attack detection method for the power Internet of Things. [PDF]

PLoS One
Cui W, Liao X, Yang Y, Feng S, Song M.
europepmc   +1 more source

Intrinsic Mitigation of the After-Gate Attack in Quantum Key Distribution through Fast-Gated Delayed Detection [PDF]

, 2019
A. Koehler-Sidki, J. F. Dynes, A. Martinez, Marco Lucamarini, G. L. Roberts, A. W. Sharpe, Zhiliang Yuan, A. J. Shields   +7 more
openalex   +1 more source

Chronological and Spatial Distribution of Skeletal Muscle Fat Replacement in FHL1‐Related Myopathies

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objectives Variants in the FHL1 gene cause FHL1‐related myopathies (FHL1‐RMs), a group of neuromuscular disorders with diverse clinical presentations. This study aimed to comprehensively characterize the spatial and temporal patterns of skeletal muscle fat replacement throughout the whole body in FHL1‐RMs, to examine disease progression over ...
Rui Shimazaki, Satoru Noguchi, Hotake Takizawa, Yasushi Oya, Yuji Takahashi, Hirofumi Komaki, Hajime Arahata, Shinichiro Hayashi, Ichizo Nishino   +8 more
wiley   +1 more source

DDoS attack detection in Edge-IIoT digital twin environment using deep learning approach. [PDF]

PeerJ Comput Sci
Al-Obeidat F, Amin A, Shuhaiber A, Haq IU.   +3 more
europepmc   +1 more source

A Comprehensive Review on Adversarial Attack Detection Analysis in Deep Learning

, 2023
Soni Kumari, Sheshang Degadwala
openalex   +1 more source

Post‐COVID Fatigue Is Associated With Reduced Cortical Thickness After Hospitalization

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Neuropsychiatric symptoms are among the most prevalent sequelae of COVID‐19, particularly among hospitalized patients. Recent research has identified volumetric brain changes associated with COVID‐19. However, it currently remains poorly understood how brain changes relate to post‐COVID fatigue and cognitive deficits.
Tim J. Hartung, Florentin Steigerwald, Amy Romanello, Cathrin Kodde, Matthias Endres, Sandra Frank, Peter Heuschmann, Philipp Koehler, Stephan Krohn, Daniel Pape, Jens Schaller, Sophia Stöcklein, Istvan Vadasz, Janne Vehreschild, Martin Witzenrath, Thomas Zoller, Carsten Finke, on behalf of the NAPKON Study Group, Y. Ahlgrimm, C. Finke, J. Fricke, T. Keil, L. Krist, N. Lisewsky, M. Mittermaier, M. Mueller‐Plathe, C. Pley, S. Schmidt, A. Stege, F. Steinbeis, S. Steinbrecher, C. Wildberg, M. Witzenrath, E. Zessin, T. Zoller, C. Arendt, C. Bellinghausen, S. Cremer, A. Groh, A. Gruenewaldt, Y. Khodamoradi, S. Klinsing, G. Rohde, M. Vehreschild, T. Vogl, S. Frank, J. C. Hellmuth, M. Huber, S. Kaeaeb, O. T. Keppler, E. Khatamzas, C. Mandel, S. Mueller, M. Muenchhoff, L. Reeh, C. Scherer, H. Stubbe, M. von Bergwelt, L. Weiss, B. Zwissler, S. Cleef, M. E. Figuera Basso, J. Franzenburg, K. Franzpoetter, A. Friedrichs, A. Hermes, J. Heyckendorf, C. Kujat, I. Lehmann, C. Maetzler, S. Meier, D. Pape, S. Poick, L. Reinke, A. K. Russ, A. M. Scheer, D. Schunk, T. Tamminga, S. Bohnet, D. Droemann, K. F. Franzen, R. Hoerster, N. Kaeding, M. Nissen, P. Parschke, J. Rupp, S. Caesar, H. Einsele, S. Frantz, A. Frey, A. Grau, K. Haas, C. Haertel, K. G. Haeusler, G. Hein, J. Herrmann, A. Horn, R. Jahns, P. Meybohm, F. A. Montellano, C. Morbach, J. Schmidt, P. Schulze, S. Stoerk, J. Volkmann, T. Bahmer, A. Hermes, M. Krawczak, W. Lieb, S. Schreiber, T. Tamminga, B. Balzuweit, S. Berger, J. Fricke, M. Hummel, A. Krannich, L. Krist, F. Kurth, J. Lienau, R. Lorbeer, C. Pley, J. Schaller, S. Schmidt, C. Thibeault, M. Witzenrath, T. Zoller, I. Bernemann, T. Illig, M. Kersting, N. Klopp, V. Kopfnagel, S. Muecke, M. Kraus, B. Lorenz‐Depiereux, G. Anton, A. Kuehn‐Steven, S. Kunze, M. K. Tauchert, K. Appel, M. Brechtel, I. Broehl, K. Fiedler, R. Geisler, S. M. Hopff, K. Knaub, C. Lee, S. Nunes de Miranda, S. Raquib, G. Sauer, M. Scherer, J. J. Vehreschild, P. Wagner, L. Wolf, J. C. Hellmuth, K. Guenther, F. Haug, J. Haug, A. Horn, M. Kohls, C. Fiessler, P. U. Heuschmann, O. Miljukov, C. Nuernberger, J. P. Reese, L. Schmidbauer, I. Chaplinskaya, S. Hanss, D. Krefting, C. Pape, M. Rainers, A. Schoneberg, N. Weinert, T. Bahls, W. Hoffmann, M. Nauck, C. Schaefer, M. Schattschneider, D. Stahl, H. Valentin, P. Heuschmann, A. L. Hofmann, S. Jiru‐Hillmann, J. P. Reese, S. Herold, P. Heuschmann, R. Heyder, W. Hoffmann, T. Illig, S. Schreiber, J. J. Vehreschild, M. Witzenrath   +190 more
wiley   +1 more source

Revolutionary hybrid ensembled deep learning model for accurate and robust side-channel attack detection in cloud computing. [PDF]

Sci Rep
Reddy CL, Malathi K.
europepmc   +1 more source

DLIIoT: A Deep Learning based Intelligent Attack Detection in IoT Networks using Cooja Simulator

Hanjabam Barun Sharma, Jasbir Singh Manhas, Vinod Sharma   +2 more
openalex   +2 more sources

Olink Proteomics Analysis Reveals Heterogeneous Responses to FcRn Blockade in Anti‐AChR Antibody‐Positive Myasthenia Gravis: FGF‐19 as a Novel Biomarker

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective This study aimed to systematically observe the clinical manifestations, immune cell subsets, and dynamic changes in serological indicators in patients with myasthenia gravis (MG) before and after efgartigimod (EFG) treatment. Methods We analyzed the baseline data, laboratory parameters, and lymphocyte subset proportions in MG ...
Tiancheng Luo, Deyou Peng, Zhouao Zhang, Mingjin Yang, Xinyan Guo, Tianyu Ma, Xiaoyu Huang, Mingming Xu, Linlin Fu, Yong Zhang   +9 more
wiley   +1 more source

Intelligent cybersecurity management in industrial IoT system using attribute reduction with collaborative deep learning enabled false data injection attack detection approach. [PDF]

Sci Rep
Alrashdi I, Duraibi S.
europepmc   +1 more source