graph neural networks

A Large Language Model Enhanced Knowledge Graph Completion Method for Nuclear Electricity Fuel Elements

A conserved molecular signature of electroacupuncture across organs: integrative reanalysis of public transcriptomic datasets

========== 1. RESEARCH QUESTION AND HYPOTHESES ========== Novelty context (verified 2026-09-28 by adversarial literature search): no prior work integrates multiple public acupuncture/electroacupuncture transcriptomic datasets at the raw-data level across organs. Nearest precedents to be cited and differentiated in the...

Capturing Temporal and Spatial Dynamics in Battery SOH Prediction with a T-GNN Model

Lithium-ion (Li-ion) batteries are widely used in industrial products. Predicting the state-of-health (SOH) of Li-ion batteries is a critical component of Prognostic and Health Management (PHM) systems. SOH prediction has been extensively researched, and many methods have been proposed for this task, including gated re...

Global Graph Surrogates and Neural Graph Elements for Computational Mechanics

ABSTRACT Machine learning surrogates are increasingly used to accelerate physics‐based simulations by predicting full‐field responses at a fraction of the computational cost. In this contribution, we present two complementary graph‐based approaches for surrogate modelling in computational mechanics. Both methods are ex...

A Comparative Analysis of Power-Delay-Area Improvements Between Traditional Heuristic and AI-Driven Electronic Design Automation Techniques

This paper presents a conceptual literature review and qualitative comparative synthesis of two major families of Electronic Design Automation (EDA) logic-optimization techniques: modern classical heuristic logic synthesis, grounded in scalable multi-level DAG-based optimization frameworks (Mishchenko et al., 2018; Ama...

IMoKGNN: Dual-Stream Fusion of Generic and Task-Specific Language Model Features for Graph Neural Networks

Text-Attributed Graphs (TAGs) are prevalent in various real-world scenarios, where each node is associated with a text attribute. Representation learning on TAGs relies on a comprehensive understanding of both the textual attributes and the topological connections. Recent works have enhanced graph neural networks (GNNs...

GRAPH NEURAL NETWORKS FOR POWER FLOW APPROXIMATION ON DISTRIBUTION GRIDS

Context. The growing integration of distributed energy resources (DERs) into power distribution networks demands fast and accurate power flow (PF) analysis for real-time monitoring, state estimation, and optimal dispatch. The Newton-Raphson (NR) method, while robust, becomes computationally expensive when invoked repea...

Redes de proveedores del Estado peruano: vínculos entidad-proveedor, concentración, dependencia y validación de sanciones, 2022–2024

This dataset contains network-based public procurement data from Peru for the period 2022–2024, prepared for the analysis of concentration, dependency, and atypical structures in state supplier networks.The database includes entity–supplier relationships, supplier-level and entity-level network measures, and consortium...

When Does Cross-Zonal Learning Help? Graph Neural Network Forecasting of Electricity Demand Across Italy’s Bidding Zones

Zonal electricity demand forecasts underpin market clearing, balancing and demand-side management, yet a market’s bidding zones are not independent: their demand co-moves through shared weather, economic activity and calendar effects. This paper asks when learning jointly across zones improves day-ahead forecasting, us...

A Deep Learning Framework for High-Dimensional Time Series Forecasting

Accurate long-term time series forecasting remains a challenging task in various real-world applications, requiring the consideration of high-dimensional features. Traditional forecasting methods often rely on manual feature selection, which can be time-consuming and subjective and may not capture the intricate relatio...

Electroacupuncture modulates cortical functional connectivity and network topology in healthy adults: an fNIRS study

Comment on egusphere-2026-3361

Abstract. Regional atmospheric trace gas inverse modelling frameworks depend on accurately simulating mole fractions, which result from transporting surface fluxes within a domain and carrying mole fractions into the region from its boundaries. With the aim of improving the computational efficiency of inverse modelling...