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**Abstract:** Distributed OpenMP implementations face significant challenges in efficiently scheduling tasks across heterogeneous compute nodes. Traditional static scheduling methods often fail to adapt to dynamic workload conditions and node availability. This paper proposes an Adaptive Hybrid Task Scheduling (AHTS) system leveraging Reinforcement Learning (RL) to dynamically optimize task assignments in distributed OpenMP environments. AHTS combines a rule-based heuristic scheduler with an RL agent, enabling rapid adaptation to varying resource profiles while maintaining reasonable scheduling guarantees.

The literature indicates that, among individuals with borderline personality disorder, pathological dissociation correlates with a wide range of impairments and difficulties in psychological function. It also predicts a poorer response to dialectical behavior therapy for borderline personality disor …

**Abstract:** This research investigates a novel approach to boosting solar energy conversion efficiency in Z-scheme heterojunction photocatalysts exceeding 20% by leveraging machine learning algorithms for compositional optimization of constituent materials. Current limitations in Z-scheme photocatalysis arise from interfacial charge recombination and suboptimal band alignment. Our method employs a hybrid machine learning model combining Bayesian Optimization and Graph Neural Networks (GNNs) to predict and optimize the composition of TiO₂, WO₃, and Cu₂O, minimizing recombination loss and maximizing light harvesting.

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