For mission-critical massive machine-type communications (mMTC) applications, the messages are required to be delivered in real-time. However, due to the weak security protection capabilities of the low-cost and low-c...
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The BRAF gene is an important signaling molecule in human cells that is involved in the regulation of cell growth,differentiation,and *** the BRAF gene mutates,it can lead to abnormal activation of the signaling pathw...
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The BRAF gene is an important signaling molecule in human cells that is involved in the regulation of cell growth,differentiation,and *** the BRAF gene mutates,it can lead to abnormal activation of the signaling pathway,which promotes cell proliferation,inhibits cell apoptosis,and ultimately contributes to the occurrence and development of *** mutations are widely present in various cancers,including malignant melanoma,thyroid cancer,colorectal cancer,non-small cell lung cancer,and hairy cell leukemia,among *** is an important target for the treatment of various solid tumors,and targeted combination therapies,represented by BRAF inhibitors,have become one of the main treatment modalities for a variety of BRAF-mutation-positive solid tumors.
Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data manag...
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Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.
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