Simulated annealing with seasonal sliced Wasserstein distance selects climate year subsets that are 2.5-3.5 times more representative than ENTSO-E practice and achieve 4-5 times effective sample size.
Combining human and artificial intelligence for enhanced AI literacy in higher education
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The authors introduce Agentivism as a learning theory for human-AI interaction that explains how durable capability develops through selective delegation, epistemic monitoring, reconstructive internalization, and transfer under reduced support.
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Bridging the climate to energy data gap: simulated annealing for representative climate year selection
Simulated annealing with seasonal sliced Wasserstein distance selects climate year subsets that are 2.5-3.5 times more representative than ENTSO-E practice and achieve 4-5 times effective sample size.
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Agentivism: a learning theory for the age of artificial intelligence
The authors introduce Agentivism as a learning theory for human-AI interaction that explains how durable capability develops through selective delegation, epistemic monitoring, reconstructive internalization, and transfer under reduced support.