AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Results indicated that both 6g and 6c showed great potential as novel SDHIs for effectively managing R. solani and S. sclerotiorum, underscoring the necessity for further research to fully explore their capabilities.
A continual-learning physics-informed neural network (CL-PINN), which combines Bayesian-optimization-based active parameter selection, task-wise dynamic loss weighting, sparse physics-constrained replay, and an optional parameter subnetwork to improve task allocation and knowledge retention under bounded active-task capacity.
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