graph neural networks

Energy-reserve coordinated scheduling of cascade hydro-photovoltaic-pumped storage systems based on physics-constrained graph neural networks

Intraday scheduling of cascade hydro-photovoltaic-pumped storage systems requires joint coordination of energy dispatch, photovoltaic accommodation, reserve security, and physical feasibility under photovoltaic fluctuation, load variation, inflow uncertainty, and limited storage capacity. Existing optimization methods...

Recognition Method for Road Grid Pattern By Integrating GraphSAGE Model and Gating Mechanism

Grid pattern recognition is of great significance for spatial pattern cognition, cartographic generalization, and multi-scale representation. To address the issues that existing methods rarely consider multi-level features and insufficiently utilize the learning and mining capabilities of intelligent models, this paper...

Toward a Transferable Representation of Protein Dynamics for Functional Prediction and Mechanistic Discovery

Protein function is determined not only by the static structure of molecules, but also by coordinated conformational fluctuations that occur across various spatial and temporal scales. Molecular dynamics simulations, elastic network models, principal component analysis, and related approaches offer useful descriptions...

Tractable and Thermodynamically Consistent pKa Prediction via First-Dissociation Aggregation

Ionization governs molecular behavior, yet predicting aqueous pKa accurately and tractably remains a fundamental challenge. Rigorous ensemble methods require enumerating a protonation-state space that grows exponentially with the number of ionizable sites, while fast graph predictors return numbers without thermodynami...

Multimodal Learning in Medical Image Analysis

Medical image analysis is progressing from a unimodal visual assessment phase toward data-driven, multimodal learning methods that reflect clinical reasoning processes more closely. Traditional deep learning approaches primarily rely on imaging data; additional information from electronic health records, genetics, or o...

Transfer of neural ensemble temperature post-processing to unobserved stations: random and spatially blocked evaluation

Hybrid GNN and Random Forest-based Drug Recommendation System using Machine Learning Techniques for Proactive Side Effect Mitigation

Adverse drug reactions and ineffective medication selection remain major challenges in modern healthcare systems, especially for patients with multiple medical conditions and varying physiological characteristics.Existing drug recommendation systems primarily focus on treatment effectiveness while giving limited import...

OpenFOAM data of the lid-driven cavity, natural-convection cavity, heated cylinder wake and data center benchmarks

OpenFOAM data of the benchmarks of the manuscript "Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields".

Breaking GNN-Based NIDS via Hard-Label Black-Box Flow Injection Attacks

Beyond Heuristics: A Research Journal on AI-Driven Logic Gate Synthesis and PPA Optimization in Modern Electronic Design Automation

Beyond Heuristics is a 3-page research journal on how Artificial Intelligence is changing logic gate synthesis in Electronic Design Automation (EDA). Modern chips contain billions of gates, and traditional rule-based synthesis struggles to balance power, performance, and area. The journal reviews three AI approaches to...

A Concise Framework for AI-Driven Blockchain: Integrating DRL Consensus and GNN Security Auditing

This research introduces a novel, high-performance hybrid framework merging Deep Reinforcement Learning (DRL) for dynamic consensus optimization with Graph Neural Networks (GNN) for advanced smart contract security auditing. Traditional blockchain architectures frequently struggle with balancing scalability and securit...

Diffusion model-driven multi-objective collaborative optimization for building energy management using graph neural networks

A generative framework driven by conditional diffusion models integrated with graph neural networks integrated with graph neural networks is proposed to solve the high-dimensional nonlinear multi-objective energy optimization in building clusters.