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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>Within philosophical aesthetics, the question of what makes an object aesthetically valuable remains a perennial one. One influential response is that of aesthetic hedonism, which reduces an object’s aesthetic value to its capacity to afford pleasure. This paper challenges the hedonic view by drawing upon an underexplored aesthetic phenomenon: aesthetic akrasia, which presents a disjunction between one’s value judgement and one’s derivation of pleasure. Situating aesthetic akrasia against the premises of hedonism reveals that hedonism not only fails to account for aesthetic akrasia, but also misconstrues the nature of the relation between aesthetic judging and liking. In developing this account, this paper demonstrates how the premises of hedonism necessitate that liking unconditionally follows from judging aesthetic value - a claim that contradicts aesthetic reality. In light of this, the paper concludes that hedonism is increasingly untenable as a theory of aesthetic value.</text>
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              <text>The Shortest (Common) Superstring problem (SSP) is a classical optimisation problem on strings (texts): to find a shortest text that contains all input texts. This problem is known to be NP-hard and therefore the main research has been concentrating on the design of fast and good approximation algorithms for it. Many constant-factor approximation algorithms have been designed, including in particular the GREEDY algorithm. The aim of this research is to understand what this algorithm does and analyse how close it gets to the optimal solution, using graph-theoretic algorithms and empirical data.</text>
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