A novel data-fusion-model SOH estimation method of Li-ion battery
Peer-Reviewed Publication
Updates every hour. Last Updated: 29-Apr-2025 04:08 ET (29-Apr-2025 08:08 GMT/UTC)
The structural materials in Molten Salt Reactors (MSRs) are subjected to a combination of challenging environmental conditions, including high temperatures, intense fluoride-salt corrosion, and severe neutron irradiation. Therefore, silicon carbide (SiC) materials, with the high-temperature strength, chemical inertness, and favorable neutron characteristics, are expected to be applied in the structural components of MSRs. However, the synergistic effect of irradiation and corrosion at high temperatures presents one of the most significant challenges limiting the safe and efficient application of SiC. Comprehensive research into the interactions is therefore required to gain a deeper understanding.
Two new quaternary CrxTi0.75Mo0.75V1.5−xAlC2 (x = 1.25, 1) MAXs and Cr0.75Ti0.75Mo0.75V0.75AlC2 are synthesized by hot pressing. Interestingly, an unprecedented transition in M-site atomic occupancy from out-of-plane order to solid solution is observed along with the composition variation, which also increases the configurational entropy from medium- to high-entropy. Through experimental observation and theoretical calculation, the influence of the atomic distribution on their properties is analyzed. Eventually, about 40% increment on the Vickers hardness than that of the Cr2TiAlC2 and low thermal conductivities are detected from the three MAXs, which can be ascribed to the solid solution strengthening effects and the enhanced scattering of both electrons and phonons from the high-entropy structure.
Faced with the persistent challenge of Non-Line-of-Sight (NLOS) errors in urban Global Navigation Satellite Systems (GNSS) navigation, researchers have introduced an innovative solution powered by Artificial Intelligence (AI).
The Chinese government’s 2020 plan for education evaluation reform emphasizes digital transformation and Big Data integration. This paper, drawing on the experience of Minzu University of China as a case study, analyses existing research and constructs a Big Data-based education evaluation model. The model considers evaluation elements (subjects, content, and methods) and processes (data acquisition, analysis, and feedback) and proposes a comprehensive approach for full business, full process, and full factor evaluation. Challenges, such as data integration and teacher training, are addressed, and practical pathways for implementation are suggested, emphasizing application scenarios, data security, and continuous teacher development.