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to simulate sewer networks as dynamic systems, targeting ≥90% modelling accuracy. Train an explainable decision-making agent to optimize interventions (e.g., pipe upgrades), balancing cost, equity, and
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to: - Developing underwater communication systems using deep learning which are well-performing to nonlinear channels. - Establishing a deep learning architecture which is optimal for underwater acoustic
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inorganic AEMWEs by integrating the perovskite electrolyte with perovskite electrodes from cell fabrication to cell configuration optimization and to single cell performance. Significance The project has
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