In this article, we study the optimal feedback control problems of knowledge dissemination processes in multilayer complex networks. First, a node-based model is established in multilayer complex networks and two collaborative control strategies are exerted to increase the scope and speed of knowledge dissemination, forming a closed-loop control system. Then, we develop a two-layer optimal control framework. At the upper level, the optimal solution of the control system is solved and sent to the lower layer. At the lower level, a model predictive controller (MPC) receives input information from the upper level and is formulated to decide on the network and then transmits it to its heterogeneous networks which can reduce control resources and computation complexity. Finally, numerical simulations are conducted to confirm the theoretical results.
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http://dx.doi.org/10.1109/TCYB.2022.3204568 | DOI Listing |
Sensors (Basel)
January 2025
Graduate School of National Science and Technology, Kanazawa University, Kanazawa 920-1192, Japan.
The development of deep learning has led to the proposal of various models for human activity recognition (HAR). Convolutional neural networks (CNNs), initially proposed for computer vision tasks, are examples of models applied to sensor data. Recently, high-performing models based on Transformers and multi-layer perceptrons (MLPs) have also been proposed.
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January 2025
Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea.
The Transformer model has received significant attention in Human Activity Recognition (HAR) due to its self-attention mechanism that captures long dependencies in time series. However, for Inertial Measurement Unit (IMU) sensor time-series signals, the Transformer model does not effectively utilize the a priori information of strong complex temporal correlations. Therefore, we proposed using multi-layer convolutional layers as a Convolutional Feature Extractor Block (CFEB).
View Article and Find Full Text PDFMaterials (Basel)
January 2025
Faculty of Chemistry and Geosciences, Vilnius University, 03225 Vilnius, Lithuania.
There is a growing focus on sustainability, characterized by making changes that anticipate future needs and adapting them to present requirements. Sustainability is reflected in various areas of materials science as well. Thus, more research is focused on the fabrication of advanced materials based on earth-abundant metals.
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January 2025
Department of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia.
We have proposed and developed a method for measuring the thermal conductivity of highly efficient thermal conductors. The measurement method was tested on pure metals with high thermal conductivity coefficients: aluminum (99.999 wt.
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January 2025
HelixHarbor Analytics, San Francisco, CA, USA.
Understanding the dynamic tumor immune microenvironment (TIME) is important in guiding immunotherapy. We have previously validated signatures predictive of checkpoint inhibitor efficacy which distinguish immunomodulatory, mesenchymal stem-like, and mesenchymal phenotypes. Here we use twenty tumor types (7162 samples) to identify potentially conserved immune biology within these TIME spaces, genes co-expressed across distinct cell types involved these immune processes, and the association of these signatures with ICI response.
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