Performance Studies on machine learning based channel modelling for vehicular visible light communication. [PDF]
Ramya L, Umadevi K.
europepmc +1 more source
A self‐sustainable vehicular vibration and indoor environment monitoring IoT platform is developed which integrates a hybrid spring‐coupled broadband energy harvester with a machine learning‐assisted PAN‐CuSCN nanofiber triboelectric vibration sensor for subtle‐to‐wide‐range vibration detection and road/speed classification.
Trilochan Bhatta +6 more
wiley +1 more source
Optimized stacked ensemble approach for detecting position falsification in VANETs. [PDF]
Saranya K, Ramakrishnan S.
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In summary, pulmonary exposure to ZnO NPs, particularly aged NPs, promotes breast cancer metastasis by remodelling the lung tumour microenvironment. Aged ZnO NPs enrich M2‐macrophages, MDSCs and activated CAFs to form an immunosuppressive metastatic niche. CXCL10 from these cells drives metastasis via Wnt/β‐catenin activation.
Yong Zhu +8 more
wiley +1 more source
A Tri-Band Omnidirectional Shark-Fin Antenna for Vehicle Applications: Design and Analysis. [PDF]
Han CZ +6 more
europepmc +1 more source
Paeoniflorin allosterically targets the STING cyclic dinucleotide‐binding domain, stabilizes an inactive open conformation, and disrupts STING–OGT interaction to reduce Thr229 O‐GlcNAcylation and downstream signaling. This mechanism restrains myeloid STING activation, alleviates psoriasis‐like inflammation, and is supported by analyses of patient ...
Fang‐Fang Zhuo +10 more
wiley +1 more source
MIRA-EDCA: Multi-Indicator Responsive Adaptation of the Contention Window for Priority-Aware Safety Broadcasting in IEEE 802.11p Vehicular Networks. [PDF]
Lazarev A +7 more
europepmc +1 more source
Adaptive client participation mechanism for federated learning in heterogeneous vehicular networks. [PDF]
Lin W, Zhou Y.
europepmc +1 more source
Deep Learning-Based Dynamic Time Division ISAC Beamforming for Vehicular Networks. [PDF]
Lim J, So J.
europepmc +1 more source
Computation Offloading Strategy Based on Multi-Agent Reinforcement Learning in Vehicular Edge Computing Networks. [PDF]
Liu Y, Sun Q, Liu Z.
europepmc +1 more source

