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Mechanical signals can provide critical early warnings for lithium-ion battery thermal runaway, achieving a lead time that outpaces existing methods by nearly 70%.
Force-feeding physics to LSTMs slashes battery thermal runaway prediction errors by over 80%, making your next e-bike less likely to explode.
Attention-based LSTMs can predict heat stress in construction workers with 95% accuracy, paving the way for proactive, data-driven safety interventions.
