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                <text>This research investigates the use of reinforcement learning to provide adaptive indoor evacuation guidance under dynamic congestion and unexpected route disruptions. A pedestrian simulation environment was developed  with a Proximal Policy Optimisation (PPO) agent trained to dynamically adjust evacuation routes in response to changing conditions. Experimental results demonstrate a 17.3% reduction in average evacuation time compared with the best fixed-routing baseline, highlighting the potential of reinforcement learning for more responsive and efficient evacuation management.</text>
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                <text>One major problem faced by engineers developing jet engines is adhering to stringent aircraft noise regulations. Engines need to be designed to minimise as much noise as possible. To solve such a problem, one cannot find the optimum conditions with pen and paper, and instead&#13;
must produce computer simulations. Current techniques used in these problems can perform poorly for waves of rapidly varying amplitude (how loud the sound waves are).&#13;
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