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Inequity aversion toward AI counterparts
Pronounced Catalytic Enhancement through Phase Partitioned Metal–Organic Framework Gas Shuttles
Presentation and characterisation of the ALBA micro8 system for targeted hyperthermia in small animal research
Rare-Earth-Catalyzed Selective Hydrosilylation of Iminocyclopropanes: Efficient Access to Azasilacyclopentanes
Impact of climate change on flowering phenology of indigenous flora in Tehsil Bhimber Azad Jammu and Kashmir Pakistan
Ultraslow Relaxation of Toroidal State in Ferrotoroidal Dysprosium Complex
Reactive co-sputtered Li–Nb–O thin films with tunable ionic conductivity and dielectric properties for energy storage applications
Achieving Record-Breaking Urea Synthesis on Crystalline–Amorphous Hybrid via Electrochemical-Chemical Looping
Relational subgraphs fused with complete subgraphs based on the knowledge graph for mining protein complexes
Abstract The potential discovery of protein complexes can elucidate the structure of protein-protein interaction networks and identify downstream regulatory genes. Given the complexity of protein-protein interactions, interpretable domain knowledge discovery has gained significant attention. In this study, we constructed a knowledge graph for interacting proteins by gathering data from UniProt and PlaPPISite databases related to the model plant Arabidopsis thaliana. We developed a relational subgraph-driven protein-protein interaction prediction model based on this knowledge graph to predict interactions within connected subgraphs. Subsequently, complete subgraphs of interacting proteins were extracted, enabling the potential discovery of protein complex structures. The knowledge graph consisted of 68,713 nodes and 109,496 semantic relationships. A total of 1,232 protein-protein interactions were predicted. Comparison with experimentally validated interactions recorded in the STRING and BioGrid databases revealed that 682 of these interactions were confirmed. Based on the predicted interactions, 336 protein complexes were identified by mining the complete subgraphs. The proposed knowledge mining method, which integrates relational subgraphs and complete subgraphs, facilitates the discovery of protein complexes and provides a novel approach for analyzing their structures and identifying downstream genes.
Li Atomic Diffusivity: A Key Descriptor for Critical Current Density and Cycling Stability in Alloy Anodes for All-Solid-State Lithium Batteries
Adaptive ε-greedy exploration for stable reconfiguration in next-gen aviation IMA systems
Abstract The next-generation aviation Integrated Modular Avionics (IMA) system adopts an architecture based on container technology, offering higher resource utilization and task configuration flexibility while increasing system reconfiguration complexity. Efficient reconfiguration strategies enhance adaptability and fault tolerance, ensuring stable operation and reduced maintenance costs. However, existing manual and heuristic-based methods struggle to meet current fault tolerance requirements. We propose an embedded container reconfiguration method using a Double Dueling DQN with Adaptive $$\varepsilon$$ -Greedy Exploration (D3QNAE), which incorporates adaptive exploration to efficiently generate stable strategies in complex environments. Experimental results demonstrate that D3QNAE reduces the first feasible solution time by 34 $$\%$$ compared to the best baseline (D3QN) in large-scale deployments (500-task scenarios), while achieving a 15.6 $$\%$$ higher maximum reward value and a 100 $$\%$$ migration impact rate under continuous faults. This method provides enhanced fault tolerance for container-based IMA systems, significantly improving stability.