Machine learning-accelerated inverse design of energy materials: A critical review of graph neural networks, physics-informed models, and generative AI for batteries, perovskite solar cells, and electrocatalysts

The socio-ecological costs of AI: Toward socially responsible and sustainable communication practices

The adoption of generative artificial intelligence among communication practitioners and researchers surged after the launch of ChatGPT in November 2022, urging practitioners to critically engage in exploring pathways for fostering socially responsible and environmentally sustainable AI practices.

Physics-informed graph neural network surrogates for real-time transmission–distribution co-optimization

Locality-Preserving Graph Laplacian Manifold Learning Based Model Predictive Control for Three-Phase Inverters

This article presents a model predictive control (MPC) strategy for three-phase inverters based on locality preserving projections (LPPs). Unlike conventional machine learning–based MPC approaches that rely on predefined or high-dimensional input features, the proposed LPP-MPC automatically extracts compact, informative representations by preserving the data’s intrinsic geometric structure. This dimensionality reduction enables fast linear control-law evaluation with computational complexity O(1), making the controller well-suited for real-time implementation. Experimental results demonstrate that the LPP-MPC achieves lower total harmonic distortion (THD) and reduced tracking error compared to quadratic-programming MPC under both linear and nonlinear load conditions, and the proposed controller maintains consistently lower THD throughout load transients than other methods such as two-degree-of-freedom MPC. Compared to existing model-free MPC and deep learning neural network, the LPP-MPC has the lowest THD and root mean square error with the least computational time owing to its efficient linear structure and strong generalization capability.

Analyzing climate risk disclosures in the banking sector using transformer models and generative AI

A fault diagnosis method for rotating machinery based on spline-modulated wavelet and energy topology-guided graph convolution neural network

A spectral heterogeneous graph neural network with multi-sensor fusion for machine fault diagnosis