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Titlebook: Mathematical Modeling and Supercomputer Technologies; 20th International C Dmitry Balandin,Konstantin Barkalov,Iosif Meyerov Conference pro

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11#
發(fā)表于 2025-3-23 11:41:39 | 只看該作者
An Architecture for Real-Time Massive Data Extraction from Social Media patterns based on online social media event detection in real-time. At its current stage the research is focused on the development of highly loaded, fault-tolerant, scalable system for social media data extraction and real-time analysis.
12#
發(fā)表于 2025-3-23 16:47:25 | 只看該作者
13#
發(fā)表于 2025-3-23 19:22:20 | 只看該作者
Conference proceedings 2021 papers presented in the volume were thoroughly reviewed and selected from the 106 submissions. They are organized in topical secions on ?computational methods for mathematical models analysis; computation in optimization and optimal control; supercomputer simulation.?.
14#
發(fā)表于 2025-3-23 23:43:55 | 只看該作者
Conference proceedings 2021 2020, held in?Nizhny Novgorod, Russia, in November 2020. Due to the COVID-19 pandemic the conference was held online.?.The 25 full papers and 8 short papers presented in the volume were thoroughly reviewed and selected from the 106 submissions. They are organized in topical secions on ?computationa
15#
發(fā)表于 2025-3-24 02:37:41 | 只看該作者
16#
發(fā)表于 2025-3-24 09:17:56 | 只看該作者
Mathematical Modeling of Multidimensional Strongly Nonlinear Dynamic Systemsd, in addition, stable operating mode is being achieved. It should be noted that the presence of several sliders-strikers contributes to a longer exposure of the processing medium to loads and, consequently, to a more efficient process of plastic deformation. The research on dynamics of mechanisms i
17#
發(fā)表于 2025-3-24 12:21:48 | 只看該作者
18#
發(fā)表于 2025-3-24 17:19:58 | 只看該作者
19#
發(fā)表于 2025-3-24 22:05:47 | 只看該作者
20#
發(fā)表于 2025-3-25 02:26:28 | 只看該作者
Node Degree Dynamics in Complex Networks Generated in Accordance with?a?Modification of the Triadic hen the nodes are selected using the preferential attachment mechanism at the triad formation step. We empirically investigate the dynamics of the mean value of node degree in the networks generated by this model and show that it follows a power law.
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